{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47</td>\n",
       "      <td>175</td>\n",
       "      <td>925200</td>\n",
       "      <td>47175925200</td>\n",
       "      <td>9252</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>227429512</td>\n",
       "      <td>1667739</td>\n",
       "      <td>...</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-85.61516 35.76106, -85.61509 35.761...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>175</td>\n",
       "      <td>925000</td>\n",
       "      <td>47175925000</td>\n",
       "      <td>9250</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>480712883</td>\n",
       "      <td>1225717</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-85.60513 35.70854, -85.60511 35.708...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>003</td>\n",
       "      <td>950201</td>\n",
       "      <td>47003950201</td>\n",
       "      <td>9502.01</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>121774227</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-86.64406 35.64029, -86.64375 35.642...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>003</td>\n",
       "      <td>950202</td>\n",
       "      <td>47003950202</td>\n",
       "      <td>9502.02</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>110617191</td>\n",
       "      <td>700793</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-86.66377 35.58189, -86.66367 35.582...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>093</td>\n",
       "      <td>003300</td>\n",
       "      <td>47093003300</td>\n",
       "      <td>33</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>5860088</td>\n",
       "      <td>229299</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-83.86208 35.99255, -83.86207 35.992...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20   NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        47        175    925200  47175925200     9252  Census Tract   G5020   \n",
       "1        47        175    925000  47175925000     9250  Census Tract   G5020   \n",
       "2        47        003    950201  47003950201  9502.01  Census Tract   G5020   \n",
       "3        47        003    950202  47003950202  9502.02  Census Tract   G5020   \n",
       "4        47        093    003300  47093003300       33  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20    ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S  227429512   1667739  ...       71        0        0       71   \n",
       "1          S  480712883   1225717  ...        0        0        0        0   \n",
       "2          S  121774227         0  ...        0        0        0        0   \n",
       "3          S  110617191    700793  ...        0        0        0        0   \n",
       "4          S    5860088    229299  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        0        0        0        0   \n",
       "1        0        0        0        0        0   \n",
       "2        0       26       26        0        0   \n",
       "3        0        0        0        0        0   \n",
       "4        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-85.61516 35.76106, -85.61509 35.761...  \n",
       "1  POLYGON ((-85.60513 35.70854, -85.60511 35.708...  \n",
       "2  POLYGON ((-86.64406 35.64029, -86.64375 35.642...  \n",
       "3  POLYGON ((-86.66377 35.58189, -86.66367 35.582...  \n",
       "4  POLYGON ((-83.86208 35.99255, -83.86207 35.992...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/tn_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cd938fd1-bf94-4c8a-bb4b-2b902e65e6a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 1701 popn tracts for TN\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"TN\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population MD voter-age population\n",
      "state pop= 6177224 VAP pct Hispanic is  0.102230809839338\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP MD\n",
      "total state pop= 6177224 , VAP pct Black is  0.29158797491777083\n"
     ]
    },
    {
     "data": {
      "image/png": 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jkvVq2iFxLjtO8GtPsB/JLDPD3Q9KdXAiSeMOb9wbjMza//vQ55ioIxJpNGqqkQxOWxQiqeQOr/4S/nM/DLg8SCQikjQ1TUhc7u7LgS7Aurj364A90hWgSIPEYjDtp0ESOfRqOOPPUUck0ugkMmrrQeCbuPebwzKRzBaLweTr4d3/gyOuh9PvhZyE1ikVkTpIZNSWedysRXePmVlUM+JFEmcGzVvDMSPh2JGasS6SIokkhCVmdgPf1kKuJVjWRCQzlWyDb76Edr3h1N8qgYikWCL1/B8CRwCfE6y4exjBroQimad4Czw5JNhPZPs3SiIiaZDIPJLVBFvdimS2bV/DuIvgs5lw5t+g+a5RRyTSJNSaSMysBXAVwZ7pFbO33P3KFMYlUjdb1sHY82HlB3DeaDjgvKgjEmkyEmnaepxguO8pwOsEG0htSmVQInX22t3w5Vy48HElEZE0SySR7O3udwCb3f1R4AzgwFrOEUmvE38FwybDfqdHHYlIk5NIIikJnzeY2QHAbkDvlEUkkqj1y+GZq4NO9eatodfhaQ+hcPl6HnhtMYXL16f93iKZIpHhv6PMrB1wB8G2t7sCd6Y0KpHafLUYHjsTir+BDcth9/5pD6Fw+XqGjp5JcWmM/Lwcxl49iAG92qU9DpGoJTJqa3T48nWgT2rDEUnAqvnw2NngMbh8aiRJBGDmkrUUl8aIOZSUxpi5ZK0SiTRJNa3+e3NNJ7r7n5IfjkgtvngPHj8H8lrAsCnQad/IQhnUpwP5eTmUlMZolpfDoD4dIotFJEo11Uhapy0KkUQ1bwMd+sK5/4T20VaQB/Rqx9irBzFzyVoG9emg2og0WRa3jFaTUFBQ4LNnz446DKmrNQuh4z7BTHV3zVgXSTMzK3T3gqo+q3bUlpm1MLPhZnamBX5mZlPM7H4z65i6cEUq+eQl+MdR8N+/Bu+VREQySk3Dfx8j2Jv9SuDfQC/gbwSTEcekOjARAOZPgvFDofP+cPClUUcjIlWoqY+kn7sfEC4ZX+Tu5XuTvmhmH6QhNmnqPhgPk66B7ofC0KehxW5RRyQiVaipRlIM4O6lwBeVPitLWUQiAF9/AZNvgN5HwmXPKYmIZLCaaiTdzewvgMW9JnzfLeWRSdPWpisMmwRdD4ZmLaOORkRqUFMi+Wnc68rDnDTsSZLPHd68F9rtCQeeD72OiDoiEUlAtYkkXKBRJD3cYcav4a0/w3cvDRKJiGQF7b0u0YvF4MWR8M4/YcAVcIYWTRDJJkokEq1YDF64Ht57AgZdB6fcrXkiIlmm1mXkzex7iZSJ1EtODuzSGY7+WY1JRMu1i2SuRPYj+WuCZXViZrlm9p6ZTQnftzezV8xsUfjcLu7YW81ssZktNLNT4soHmNnc8LO/mOlP2axRuh3Wfhq8PuFOOP62GpPI0NEz+ePLCxk6eqaSiUiGqWmJlMPN7CdAJzO7Oe7xKyA3Cff+MbAg7v1IYIa79wVmhO8xs37AEII9408F/m5m5fd/EBgB9A0fpyYhLkm14i0w/hJ4+BTYtrHWpqyqlmsXkcxRU40kn2ATqzyClYDLH18DDRpSY2bdCbbsHR1XfBZQPlLsUeDsuPLx7r7d3ZcCi4GBZtYFaOPub3uw8uRjcedIptq+CcZeAItnBDWRBCYali/XnmtouXaRDFTT8N/XgdfNbIy7L0/yfe8DfsaOS9Xv7u4rw3uvNLPOYXk3YGbccUVhWUn4unL5TsxsBEHNhZ49eyYhfKmXrevhifODPUXOG53wEF8t1y6S2RLpIxltZm3L35hZOzN7qb43NLPBwGp3L0z0lCrKvIbynQvdR7l7gbsXdOrUKcHbStK9/nv48kO48LE6zxMZ0Ksd1x23t5KISAZKZPhvR3ffUP7G3dfH1Rbq43vAmWZ2OtACaGNmTwCrzKxLWBvpAqwOjy8CesSd351g7a+i8HXlcslUx98B/c6GnodFHYmIJFEiNZKYmVW0B5lZL6r5yz8R7n6ru3d3994Enej/cvdLgcnA8PCw4cDz4evJwBAza25mexJ0qr8TNoNtMrNB4WitYXHnSKbYsAImDA861fNbKYmINEKJ1EhuA94ys9fD90cT9jck2T3ABDO7ClgBXADg7vPNbALwEVAKXOfu5asPX0OwN0pLYHr4kEyx9lN49Mygg339MujynagjEpEUSGir3XBHxEEE/RJvu/tXqQ4sVbTVbpqsXgCPnQWx0mAZeCURkaxWr612Kykj6LPYCPQzs6OTFZw0Qis/hEdOBwwunxZpEtGMeJHUq7Vpy8yuJpg82B14n6Bm8jZwfEojk+zVsi102g/O+ht02CuyMMpnxBeXxsjPy2Hs1YM06kskBRKpkfwYOBRY7u7HAQcDa1IalWSn1QuCRRjb9oQrpkWaREAz4kXSJZFEss3dtwGYWXN3/xjYN7VhSdZZ9AqMOhbe+mPwvp7LniWzKUoz4kXSI5FRW0XhhMRJwCtmth7N15B4H02GiVdC5/1hwJX1vkyym6I0I14kPWpNJO5+TvjyV2b2GrAb8GJKo5Ls8cFTMOka6DYAhj4d9I/UU1VNUQ395T+gVzslEJEUqzGRmFkO8KG7HwAV62+JBL5ZDVNuDPZWv3g8NN+1QZcrb4oqKY2pKUoki9SYSNw9ZmYfmFlPd1+RrqAkS+zaGYY9D3scCM1aNvhyaooSyU6J9JF0Aeab2TvA5vJCdz8zZVFJZnvjXmi9Bxx8KfQYmNRLqylKJPskkkh+nfIoJDu4w79+A2/+Eb5zMXx3qPZXF5GEEsnp7v7z+AIz+x2g/pKmxB1evBVmPQiHDIfBf1YSEREgsXkkJ1VRdlqyA5EM5h50qs96EA67Br5/P+RUvdtybfNAtGSJSONTbY3EzK4BrgX6mNmHcR+1Bv6T6sAkg5hBm+5w1C1w/O3V1kRqmweiJUtEGqeamrbGESzL/ltgZFz5Jndfl9KoJDOUFsP6pdBpXzjmp7UeXts8kFTMExGR6NW0Z/tGgtV+L05fOJIxSrbCU5fB57Ph+jnQqn21hxYuX8/MJWtp1yq/xnkgmici0jgl0tkuTc32b+DJIbDsLfj+fbUmkfjmqjsH92f9luIq54FonohI46REIjvaugHGXgCfF8K5o+CgC2s8vHJz1fotxVx33N7VHq95IiKNjxKJ7OitP8EX78EFY6Bf7XNO1VwlIkoksqPjboP9Btdpxvp5h3THw2fVNkSankS32pXGbMNnMH4obFkHec0TTiLl/SNPvrOCZ+cUpThIEclUSiRN3dpP4ZHTYOmbsGF5nU7VDoQiAkokTdvqj+GR06F4MwyfDF0PrtPp2oFQREB9JE3Xl/PgsTMhJy/YX73z/nW+hIbziggokTRdrdpD537Bulkd9qr3ZTScV0TUtNXUfDkPykqhTVe4fEqDkogWYBQRUI2kaVn0Kjw1FI64AY6/rUGX0gKMIlJONZKmYsGUYNmTjn3hsP9p8OWemVPE9pJgxNb2khjPVDH8VzUWkaZBNZKmYO5EeHZEMCrr0onQMqg5lC+2WF1HeXWfFy5fz8TCIjx878DEwqIdJiSqxiLSdCiRNHab18ILN0LPw+GS8dC8NZDY3iEX/9/MiqVPnvzBt5/PXLKW0rLYDrcpK9txWXgtGS/SdCiRNHa7dIDhz0On/SG/VUUt4/MNW2v8Rf/snCKKS4NkUVwa49k5RRWfl88fKT8/h53nkWgNLpGmI+2JxMx6AI8BewAxYJS7329m7YGngN7AMuBCd18fnnMrcBVQBtzg7i+F5QOAMUBLYBrwY3d3BN78U1D7GPgD6DYA2LEWkpebQ16OURbzKn/RV/4mxr+Pnz/SrlV+lcvGa46JSNMRRY2kFPiJu88xs9ZAoZm9AlwOzHD3e8xsJMGujD83s37AEKA/0BV41cz2cfcy4EFgBDCTIJGcSrCrY9PlDq/dDW/8AQ68EA69umJr3PjmprKyGEMG9qRr25ZV/qI/75DuTJz9GSVlTrNc47xDuu/weSLzR1Ixx6S2fh0RSb+0JxJ3XwmsDF9vMrMFQDfgLODY8LBHgX8DPw/Lx7v7dmCpmS0GBprZMqCNu78NYGaPAWfTlBOJO7x0G8x8AA6+LJhsGLe/euXmpnNrWK13QK92PDni8Iz6pa0OfJHMFGkfiZn1Bg4GZgG7h0kGd19pZp3Dw7oR1DjKFYVlJeHryuVV3WcEQc2Fnj17JvEryCDuMPVmmP0wHPZDOOW3FH62cadEcO4h3bHwGeCB1xZXmygybda6OvBFMlNkicTMdgWeAW50968t7i/nyodWUeY1lO9c6D4KGAVQUFDQOPtQzKD9XnDkzXDCnRSu2FDx13tujtGrfSuWr9tCWczJz8uhf9fduGvK/Ir+kvMHdM/4/UTUgS+SmSJJJGbWjCCJjHX3Z8PiVWbWJayNdAFWh+VFQI+407sDX4Tl3asob1pKi2HtIti9Pxzxo4riZ8MJgw7EypzFazZXfFZSGmP6vJUVf90Xl8Z4clawp0gmNxepA18kM0UxasuAh4AF7v6nuI8mA8OBe8Ln5+PKx5nZnwg62/sC77h7mZltMrNBBE1jw4C/punLyAwlW2HCMFgxk2e+9wKTFm3ntAO6sGLtZsbNWlF19SzUv0sb3l22riLZODs2F9U0GTHKX+SZ1twmItHUSL4HXAbMNbP3w7JfECSQCWZ2FbACuADA3eeb2QTgI4IRX9eFI7YAruHb4b/TaUod7du/gfEX40vfZFSbH/HbaZ8D8Oair2o9tcxhzNvLuHNwf/69cDUzFqzC/du5INV1aquzW0SqEsWorbeoun8D4IRqzrkbuLuK8tnAAcmLLkts28g3D59Dq9XvcXPxNUxafXidL1FcGmPeFxt5Y9EaHMjJMe4c3J8BvdrxwGuLq+zUVme3iFRFizZmoS+m30v+qve5tvgGJsWOrNc1zAyDisTg7qzfUgxUv/OhdkQUkapoiZQs9Hc/l3nFHXnf9074nG5tW7Dq622UViyR5fTvuluVo6Cq69TOlM7uqPtpRLJRKn9urKmtKFJQUOCzZ8+OOoy621gEU3/CB4f8huFPLWXD1tI6nZ6fl8NeHXdhwZebKspO7rc7nVo3xyHjh/6WUz+NSN0l4+fGzArdvaCqz1QjyQbrlrD94cHENm/gznkvsiFW910NS8tifByXRABeXbAKCJJM5SVQMpX6aUTqLtU/N+ojyXRrPmHLP09hy6aNXLDtVj6oRxIpV7nuGXN2+I+VDdRPI1J3qf65UY0kk636iJJHBrN5WylDi+/gE+9R+znViFXRgtks14hVs/pvpsqUfhqRbJLqnxslkky2Syc+ivXkxuJLWepdknppAy4o6EG3alb/TaZkd/KVT0os38pXCUWkdqmczKtEkom+nEvh1t3555srePnrn6bkFrk5lpYO9lR1jqvTXSRzqI8k0yx+lZJRJ/DuQzfz8kerUnKLHIOrj9yTmUvWUrh8fUruUa6qTr5Mvq6I1J1qJJnk46mUPjWcRWVdGVV6Rspuk5NjPPyfpZSGKwGn8q/5VK3Yq5WARTKHEkmGWPLao/R6/UbmxvowvPhnfM2uSb1+q/wctpWU747olBGM4tpesuN+7MmWqk4+dbqLZA4lkgzw58mzuLJwJLN9H64s/imbaZn0exSXOXm5OZSVxcjNzSHmTmmZ48C4WSvo33U3Ljlsx02/auskT6QTPZWzabUSsEhmUCKJ2LCHZvHGoq94xW5niXdhG82Tct3yVTHLR/3GYs6FA78dpXXP9AW8u2x9xTG3T5oLwPotxRXNRDV1ZifS2a0OcZGmQYkkQuP//BP2+Worb3AGH3nvpF770N7tcKhIFjGHNs3zuO64YH2u4m8X3ar4/I7n5+HuFTPda5oJG9/ZXVwS475XP+HGE/ep9phE9joRkeykUVtRcOfFv97AkI2jOTBnKdXsENwg7y5bv9OIrNFvLa0ou+jQnfeuL4t5xS99hxpnwpZ3ducAMeA/i79i6OiZO9yzqtm05bWUP768cKfjRSQ7KZGkmzv/+usITl37KE+VHstNJddS/fYsDbgNO89mL4059736CYXL13PJYT354dF92LV57k7nls8xGXv1IG4+eV/GXj0IgAdeW1zxi7+8s/t7fTuSY1UvtVJ+TPk1qtvTRESym5q20qhw+Xo+fmgEQ3NeZkzpyfy6dBie5lz+1qKveHfZOi4/vDej31pKWZhtjLh6kRkLv9xUa3/JgF7tuPHEfXh32bpqh+FW7hDXsF2RxkfLyKfJPdMW8I83lnBp7it0s6/4XekQUlETSVR84jCgV4dWrFi3hZgHExZzzIjF9Zc8+c6Kis++t3fHHfpD6trn0ZA+EvWviESjpmXklUjS4IrRb7Hq0w+S3qGeLDkGI47qw5i3l1FSGsPMKIsFQ4NzDY7ff3de+3h1RZkBzZulfxSWRoGJRKemRKI+khQ7/rfTGbr8dp7O/zWdyMyO5ZjDmLeXcefg/tx88r7cddYBNG8WdJLn5hivf7KGmDsWVqCcnfs3yhdQTGXnufpXRDKT+khS6JDbn+N+u5ejcudxW8mVrCFz/3reXhJj3hcb6da2Jfvu0bpi1vgXG7bu0KyVl2O477j0fLpqCupfEclMSiQp8p1bn2Z0s99ziC3i5uIf8mzs6KhDqpEDT8/+jLKYk2PG1UfuSeuWzXba1/3Owf0rOuHLk0W6di3UsigimUmJJAV6j5zKdbkv8137lOtLrmdabFDUISWkpCzoL4u58483llT0hVSVPOKls6agZVFEMo8SSZL1HjkVgAfLzuTN2IF86PXfGjdq5X0h67cUc91xe1e7kZRqCiJNmxJJkhQuX891D77AI81GM7LkB6yifVYlkR3mkcSVmRntWuXX2g+imoJI06VRW0kwbtYKbvzHczydfxcDcj6hq2X3aCIzOLpvR3Jzgrkkd02Zz7NzimodMZWOkVsiknlUI2mgcbNW8NCkF5mQ/1taUMzQ4tuY632iDqvO4msj7vDfT9dWzBuJX3urun4QzfEQabqUSBpg3KwVPDxpOuPz/xeAIcW3s9B3XgwxG8XcyY0b6ntA192AoLnr3Cr2ek/XyC0RyTxKJPVUuHw9v3huLrvRlrmxPvym9DKWepeow0qKHAtqH3cO7s/8LzayZtN2fjV5HiVlQXLp33W3nZKE5niINF1KJPU09eWXyKcFG9mVK0t+FnU4SZGbY3z/oC6s3VzMaQd0Yd89WnPXlPlsL4lVNH2Vxpw7n5/Hvnu01sgtEQGUSOrlk/9O5qefX0/3vOO5q3RY1OEkjbsz5cOVxNx5d9k6zg03t6o8mivmXmXTlUZuiTRNWT9qy8xONbOFZrbYzEam8l6Fy9fzu/v+RK+XrmBZbA8eKD0rlbdLm2a5Rm644m/85lbGt5tb5eUauTlW0eylpisRKZfVNRIzywUeAE4CioB3zWyyu3+U7Hv1HjmVM3Jmcl+zB5jvvRhePJKN7Jrs26Rd97YtuP/iQ5i5ZC3tWuVz15T5Ff0c5x7SnXMP6V7RXAWo6UpEdpLViQQYCCx29yUAZjYeOAtIaiLpPXIqu7KFu5o9whzvy1XFt/ANrZJ5i8hce1zfHZqk9t2j9U7JonJfiIhIvGxPJN2Az+LeFwGHVT7IzEYAIwB69qzf8NxvaMXFxbfzmXdiKy3qdY1M0q1tC647ri+XHLbj90P9HCJSV9meSKraYnCnnbrcfRQwCoKNrep7s0+8R31PzRidds3nppP23SmBiIjUV7YnkiIg/rd7d+CLZN9k2T1nVCzGWK5j63y2bC9jl/xcNm8vZUtJjFyDsjRtONkyL4fObVrw1TfbyTEYelgvenbYhenzVtK/Sxtat2xGu1b5rN9SXPGsvg0RSYVsTyTvAn3NbE/gc2AIcEkqbrTsnjNScdmkU01DRNItqxOJu5ea2Y+Al4Bc4GF3nx9xWCIiTUpWJxIAd58GTIs6DhGRpirrJySKiEi0lEhERKRBlEhERKRBlEhERKRBzD1NEx8yhJmtAZbX8/SOwFdJDCdVsiFOxZg82RCnYkyeqOLs5e6dqvqgySWShjCz2e5eEHUctcmGOBVj8mRDnIoxeTIxTjVtiYhIgyiRiIhIgyiR1M2oqANIUDbEqRiTJxviVIzJk3Fxqo9EREQaRDUSERFpECUSERFpECWSBJnZqWa20MwWm9nINN+7h5m9ZmYLzGy+mf04LG9vZq+Y2aLwuV3cObeGsS40s1PiygeY2dzws7+YWVWbgzUk1lwze8/MpmRijGbW1swmmtnH4ffz8AyM8abw33memT1pZi0yIUYze9jMVpvZvLiypMVlZs3N7KmwfJaZ9U5SjH8I/70/NLPnzKxtlDFWF2fcZ7eYmZtZx6jjTJi761HLg2CJ+k+BPkA+8AHQL4337wIcEr5uDXwC9AN+D4wMy0cCvwtf9wtjbA7sGcaeG372DnA4we6S04HTkhzrzcA4YEr4PqNiBB4Frg5f5wNtMylGgu2jlwItw/cTgMszIUbgaOAQYF5cWdLiAq4F/hG+HgI8laQYTwbywte/izrG6uIMy3sQbIuxHOgYdZwJfz2pvHhjeYT/UC/Fvb8VuDXCeJ4HTgIWAl3Csi7AwqriC/9jHh4e83Fc+cXAP5MYV3dgBnA83yaSjIkRaEPwS9oqlWdSjN2Az4D2BNs8TAl/EWZEjEBvdvwlnbS4yo8JX+cRzN62hsZY6bNzgLFRx1hdnMBE4DvAMr5NJJHGmchDTVuJKf/hLlcUlqVdWEU9GJgF7O7uKwHC587hYdXF2y18Xbk8We4DfgbE4soyKcY+wBrgkbD5bbSZ7ZJJMbr758C9wApgJbDR3V/OpBgrSWZcFee4eymwEeiQ5HivJPjLPeNiNLMzgc/d/YNKH2VUnFVRIklMVW3LaR83bWa7As8AN7r71zUdWkWZ11CejNgGA6vdvTDRU6qJJZXf6zyC5oQH3f1gYDNBc0x1ovg+tgPOImjC6ArsYmaX1nRKNbFE/X+2PnGlNGYzuw0oBcbWcr+0x2hmrYDbgDur+riae0b2vaxMiSQxRQRtl+W6A1+kMwAza0aQRMa6+7Nh8Soz6xJ+3gVYHZZXF29R+LpyeTJ8DzjTzJYB44HjzeyJDIuxCChy91nh+4kEiSWTYjwRWOrua9y9BHgWOCLDYoyXzLgqzjGzPGA3YF0ygjSz4cBgYKiH7T0ZFuNeBH88fBD+DHUH5pjZHhkWZ5WUSBLzLtDXzPY0s3yCzqvJ6bp5OBLjIWCBu/8p7qPJwPDw9XCCvpPy8iHhyI09gb7AO2HTwyYzGxRec1jcOQ3i7re6e3d3703w/fmXu1+aYTF+CXxmZvuGRScAH2VSjARNWoPMrFV47ROABRkWY7xkxhV/rfMJ/g8l46/9U4GfA2e6+5ZKsWdEjO4+1907u3vv8GeoiGCAzZeZFGdNX4AeiXWMnU4wWupT4LY03/tIgmrph8D74eN0gjbPGcCi8Ll93Dm3hbEuJG60DlAAzAs/+xsp6IADjuXbzvaMihH4LjA7/F5OAtplYIy/Bj4Or/84wWidyGMEniTotykh+EV3VTLjAloATwOLCUYj9UlSjIsJ+gvKf3b+EWWM1cVZ6fNlhJ3tUcaZ6ENLpIiISIOoaUtERBpEiURERBpEiURERBpEiURERBpEiURERBpEiUSkjixYQfjaJF7vWDM7oory3mZWZGY5lcrfN7OB4eubzGybme1W6Xobw2VgFpjZL5MVq0hVlEhE6q4tweqqOzGz3Hpc71iC2es7cPdlBPMfjoq7/n5Aa3d/Jyy6mGDC7DmVTn/Tg2VgCoBLzWxAPeISSYgSiUjd3QPsFdYM/hDWAF4zs3HAXAAzm2RmhRbsKzKi/EQL9rWZY2YfmNmMcBHOHwI3hdc7qtK9niRYKaDckLAMM9sL2BW4nSCh7MTdNwOFBEtwiKSEJiSK1FH4y3+Kux8Qvj8WmAoc4O5Lw7L27r7OzFoS1BiOIfjDbQ5wtLsvjTvmV8A37n5vFffaA3gP6OHupWa2ALjA3eeZ2e0Ei/PdDSwBBrr76jCeW9x9sJl1IEgkZ7j7/BR9S6SJy4s6AJFG4p3yJBK6wczKm5t6EKyP1Al4o/w4d691ET13/9LM5gMnmNkqoMTdy3fVGwKc4+4xM3sWuAB4IPzsKDN7j2BJ/3uURCSVlEhEkmNz+YuwRnAiwcZCW8zs3wRrHxn1W8q7vHlrFd82ax1EkJxeCdbrI5+gVlKeSN5098H1uJdInamPRKTuNhFseVyd3YD1YRLZDxgUlr8NHBOu4IqZtU/wes8QLNJ5EcES/RD0ifzKw9Vi3b0r0M3MetXrKxJpACUSkTpy97XAf8xsnpn9oYpDXgTyzOxD4DfAzPC8NcAI4Fkz+wB4Kjz+BeCcajrbcfcN4TVWxTWfDQGeq3Toc+zYMS+SFupsFxGRBlGNREREGkSJREREGkSJREREGkSJREREGkSJREREGkSJREREGkSJREREGuT/A3Qm3oCciy9VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for TN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for TN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "612 4376 0.03886622884199991 nonPolygon tract no, pop, area\n",
      "0.00021106174249997716\n",
      "0.03865516709949993\n",
      "749 2640 0.003706850693999965 nonPolygon tract no, pop, area\n",
      "6.2864300000113605e-06\n",
      "0.0037005642639999538\n",
      "965 4695 0.026006760042500163 nonPolygon tract no, pop, area\n",
      "0.01943334505200004\n",
      "0.006573414990500123\n",
      "1230 3796 0.019827977167999965 nonPolygon tract no, pop, area\n",
      "2.958669999998504e-05\n",
      "0.019798390467999978\n",
      "1405 2989 0.013519196238999914 nonPolygon tract no, pop, area\n",
      "7.200703399992877e-05\n",
      "0.0001451611335000273\n",
      "4.0081106999996494e-05\n",
      "0.013261946964499961\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "69edb6b2-7b88-43ca-8017-ff4d271ce3e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oRaRk7RrchePM7NsNVVPG1Kdv1Q0ANMx7Yx4rm1gU9CJSstr6XwZgViIGjO1CZb31MwFoqDrE5RImGQW9iJSs9vYVQPY69DMPuqNpTn0N0wForGjMV1kTjm6sKCIla2f3ZgLutPzlf8Pxy8bUp7eqEYZ301DRkN/iJhDN6EWkZHXEepiWShGadlLmEgdj0FuROca+IaKgf4WCXkRKVsfIHlpSKag9Zsx9+hJRABoqFfSvUNCLSMnqSMdoSaYgHh1zn754lFqHsI6j30tBLyIla3dyiOZUCsZxgbLe5CANrmjbl/5viEhJGk4OM5COcUwyBZHaMffrTY3QqNn8fhT0IlKSOoc6AWixMIzjLNc+T9AYGMPlEiYRBb2IlKTdQ7sBaEmlx9Wv11M0hEa5uuUkpaAXkZLU8cyPADgmNo4biKTT9AWgcRxLPZOBgl5EStLIy5mzYivGEdrJkR4GAgEdQ38ABb2IlKQ5l34bgJfnnz/mPv19mWvjNFROzUtNE5WCXkRK0tz6YwHYtuVO2PXc4Tv0bKM3e7ORxupp+SxtwtG1bkSkJLVUt1CVTrMhEobv/hkcswiuvhsqcizl7F4H3zmbvooIzJxOY3VL4QsuYZrRi0hJCliAN8Xh9rpaVlZWwO61cPO7Dm7YtRl+chkAvcEgAI21MwpZask7bNCbWaWZPWlmz5rZOjP7fHb758xsh5mtzj4uHqX/x7P91prZz82s8mgPQkTK0yfffQezQrV8cOYs7q6ugu2PwaZ7Xm3QvRV+/FYYzBxz3xvIRFpD/exilFuyxjKjjwHL3P00YAmw3MzOyu77mrsvyT7uPLCjmc0C/g5odfdFQBC4/OiULiLlblrjcdz0zrs4pXkxnzymhZ/MOhFu/Wvo2gJ9bfCTt0JyGN70TwD0ZYO+seG4YpZdcg4b9J7xyhWFwtnH2G71khECqswsBFQD7eOuUkQmrcbKRq6/8HrOn3s+10VG+I+GatL/dRZ8sxWGe+GKX8OMJQD0BgOEgJox3l92shjTGr2ZBc1sNdAB3OvuK7K7PmJma8zsRjM76IaO7r4D+DKwHdgJ9Ln7PQe2y77GtWa20sxWdnZ2HslYRKRMVYYq+fJ5X+a9C9/LT2vCfHJaAzFPw5W3Z+481XISAL2BIPWBCmyM166fLMYU9O6ecvclwGxgqZktAr4DnEBmOWcn8JUD+2XD/zLgOGAmUGNm7xvlNa5391Z3b21ubj6CoYhIOQsGgnz6jE/zidZPcE9NNdcuWUbftBMyOxvnAtAfDNBYN6uIVZamcR114+69wAPAcnffnf0FkAa+DyzN0eUCYKu7d7p7ArgNOPu1lSwik5WZcdUpV3HdudfxXO8mrvz9lbRH21m7o493xT7LaptB36CuXHmgsRx102xmjdnnVWTCe4OZ7Xv80tuAtTm6bwfOMrNqy/wtdT6w/jVXLSKT2vLjlvO9C79H53An773zvTzbsY6n/CR2BaqIDunKlQcay4x+BnC/ma0BniKzRn8H8CUzey67/U3AxwHMbKaZ3QmQXcv/FfA08Fz29a4/+sMQkcnmjOln8JPlPyEUCPGN5z9OsGYTFhxieERBfyBzH88BNIXR2trqK1euLHYZIjIBdAx18KH7PsSmns04aZqSy3ngA9cVu6yCM7NV7t6aa5/OjBWRCa2luoUfLf8R8+pOBGAw9EyRKyo9CnoRmfBqI7X86OLvAfCpMz9W5GoO78U7H2e4u69gr6eLmolIWZhaOZXnrhrDVS6LbNeKDUQeSvLSqkdY+C+XFOQ1NaMXESmgvse3A1A3WE+yL1aQ11TQi4gUiCfTRHYH2RNrJ+1p2m97uiCvq6AXESmQwXUdhD1C19QOtg+uh80JUoOJvL+ugl5EpEB6HtnKSGqQOReczobeJwikjehj+b/Oo4JeRKQA0kMJaEuwfXADs09ZTF9yD0ONQ0QfbSc9kszrayvoRUQKYOi5LsyNwaZBqusbqJ/WwsvBLfhIksEVO/P62gp6EZECiD7VTl+8i2mnHQ9ARXU1q1f+nsTUFLt/9zypWP7W6hX0IiJ5Ft3eRbJtiJeiazn21NMBCFdk7qr61IY7qAxU07uhLW+vr6AXEcmzbbevwN3pq+lh+gnzATih9UwABhLdAITi+Tt/VWfGiojkWWgn9NHFe7721b3bFi+7iHQqxZO3/BKAwFD+7oqlGb2ISB6l+uPUpOsZqh3cb3tVXT1nvf3dJDxOgjjJ7uG81aCgFxHJo5FNPQAM1Q2O2iaa6CHZk7/LISjoRUTyKLp2F8PJKCORkZz755y8mGi8h5GdvXmrQUEvIpInnnZiW/rYNbyVlnnH5Wzz+ne9h2iiF+9Pkk6l81KHgl5EJE/ibQMEksbO4Repm9acs82ckxczY8nJBCzISFd+rlGvoBcRyZORjT04sHv4JXa/+MKo7VKBzCUQkgP5WadX0IuI5En3k7vojg8ST4+QTMT3bk/G4zx775307t6VadeTubBZKJ2fI951HL2ISB6kBhOEBuK0D2auOX/6my8hnU5hFuDBn97I6rvvAOBjN91G7Ywm6IGRrn6qT5x21GvRjF5EJA9im3swYNfQVoLhMD/51Ee54+v/wcrf3rY35AF+/i+fpHZ6Jtz7tufn4mYKehGRPOh4dBOx1BA98V38xWf/nfjwMJtXPMZDN/+QSFX13nY1U6YQrIsAEMzT0o2CXkTkKPO0k3p5mF3DLzH/rHNomj1nv/3zl57NlV/6JgCxoSHat2wknhoh0TuUl3q0Ri8icpQNbN1NBZUM1w1z6cf/kRs++gEAQpEKrvn2jVTXNwAQqaqmfePzhMIhjklPo7FyzqG+7RHTjF5E5CjrfHErAFVNDZmrVnbsBuCab/1gb8gDzF54CgDb164hlh5heCT32bOvlYJeROQoS8Qygd3f1clXL7907/bBvt792s08ceHe5x2Vvby8+6W81KOgFxE5yhqPmQFA3+79j6IZ7O3Z7+vjTm9l7qJTqZk9nYHqFLWpavJBQS8icpRVNtYCUB9u2m97+8bn9/u6Zd7xvO1D/8I7l3ycMwcX8/Ixe/JSjz6MFRE5yhrnzyY6q42T0meyuf9pkp45K7Zr+7a9bdKxJAP3v8zAwzuwoBFe1sIlbzwzL/Uo6EVE8qDlbafQ8a3VXMIibidzdmx9czOedoae7qDv7q2kBxJUv66FhuXzCNZX5K0WBb2ISB5EZtdRdUoTnvgzFj7zKOubq5gz7WQ6/ms1ibYokTl1NFxxMhVz6/Ney2GD3swqgYeAimz7X7n7/zWzzwHXAJ3Zpv/H3e/M0b8RuAFYBDhwtbs/flSqFxEpYfUXHcvQ2k5OnvduFtdVYw+lSdXHmfLuBVSf1owF8nef2H2NZUYfA5a5e9TMwsAjZvb77L6vufuXD9P/P4G73P2dZhYB8vOxsohIiQkfU0OooQ+zzGGUdcvmUHfeHAIVwYLWcdijbjwjmv0ynH34WL65mdUD5wI/yH6vuLv3HlmpIiITz/TPXEqo2Wm8bA4NF80reMjDGA+vNLOgma0GOoB73X1FdtdHzGyNmd1oZlNydD2ezNLOD83sGTO7wcxqRnmNa81spZmt7OzszNVERGTCMTOm/8O51L5+XtFqGFPQu3vK3ZcAs4GlZrYI+A5wArAE2Al8JUfXEPA64DvufjowCPzjKK9xvbu3untrc3PuW26JiMj4jeuEqeyyywPAcnffnf0FkAa+DyzN0aUNaNvnL4BfkQl+EREpkMMGvZk1Z4+cwcyqgAuADWY2Y59mbwPWHtjX3XcBL5vZguym84HnD2wnIiL5M5ajbmYAPzazIJlfDLe4+x1mdpOZLSHzwexLwAcBzGwmcIO7X5zt/1Hg5uwRNy8C7z+6QxARkUM5bNC7+xrg9BzbrxilfTtw8T5frwZaj7xEERF5LXRRMxGRMjfpgr5z00NsefSbJGODxS5FRKQgJtW1bl588rtsHfgymDP0wFZOffNXi12SiEjeTZoZfdeWR9gavY7a3tcxZfBNdAbvYGhPW7HLEhHJu0kT9O3bbsWSFbzuohv4kyX/AIEU25/7SbHLEhHJu0kT9P2pp6mJnky4rp76OQupiZ5KR+x23FPFLk1EJK8mRdCPxHYRC7VRmzht77bplW8nEe6ia/cfi1iZiEj+TYqg7+l5AoB6f/V0gKbGNxEcaaSt7WfFKktEpCAmR9B3P0YgUUtt9YK92ywdpHHHuXT3P8zwsD6UFZHyVfZB7+50dz9OdfdJhBqrXt2eSNPQdh5gtLf/ongFiojkWdkH/fDwdmLxdqq7FxJsiOzdHpldSzjWRGP6LHa030I6HS9ilSIi+VP2Qd/Tk7k9bfWehVjw1eFWHNdA9ekt1Kw5i0RiD52d9xarRBGRvJoUQR8JNRMZmoGn978DYmRuHTUdi6gIzuDlrTcVqUIRkfwq66B3d7p7Hqex9kwMw5Pp/faHZ9RgBKhbfw59Q08R3bO5SJWKiORPWQf94OBmEok9TG06G4D0YGK//RXzGqhcMIWaPYsB6Gt/ruA1iojkW1kH/Svr81NazoGQkRo4+APXKX+xgNoT5gEQG+woZHkiIgVR9kFfVTmX6qrZhJqqSHYMH9QmWBOm6ZLMbWxjI52FLlFEJO/KNujdU/T0rmDKlLOAzHp8Ymfua9CHaqoIxmtJJPYUskQRkYIo26AfGFhHMtlPf/9MRkZGqJhTR6ovRmLXwWFvASOUaiSe6CpCpSIi+VW2Qb9t+z0A3HdfG7feeitVS1ogaAw+tStn+0CillQwWsgSRUQKomyDfteuBxgcbODYYxfz4osvko5A5YKpDK87eHnG3QnEK0kFh4pQqYhIfpVl0GcuZ7AF9xM544wzSKVStLW1EZlbR6o3Rnpo/8MscQgkKkmbgl5Eyk9ZBn1//xoCgQTh8GJaWloA6OnpITKzFoD4AR/KWsAIpqtJohuGi0j5Kcug79rzCO5QXXU6iURm9h4Ohwk1VQKQ6hk5qE8oWEeaIdz9oH0iIhNZWQb9nj2PEY1Opb5+OkNDmeWYmpoagg0VAKT6Dj5xKlzbiFtSV7EUkbJTdkGfSg0zOLiG3t7p1NXV7Q366upqLBQgUBsm1Rc7uGMkc+9Ys2AhyxURybuyC/revlW4J+jLEfQAgcoQ6ZHkQf0SIz0EUjUEAqGC1isikm9lF/Q93Y/hHsD9BKZNm8bISGY9vrIysz5v4QCeSB/ULxHvI+R1Ba1VRKQQyi7od+16kP7+Js4+exmh0Kuz81c+ZLVw4KDLFafjKZLeTyjYUNBaRUQKoayCPpkcYCS2kaGhuZx++ukA1NVlZun9/f14ykn2xghE9l+HTw8nSYUHCYcU9CJSfspqQXrT5t9h5hw3bznhcBiAxsZGAPr6+qhpc9L9capf17JfPwNS4UGCzCtswSIiBVBWQd/ZuRqAU099K+l0mkAgwNSpUwHYtb2d6kc7Cc+po3Jh03793CEdHiRs9YUuWUQk7w67dGNmlWb2pJk9a2brzOzz2e2fM7MdZrY6+7j4EN8jaGbPmNkdR7P4AyWTPaRSIZ5+eh1f/OIXWbFiBbW1tVRWVtL25At4Is3Ud87HArZfv3QiSSocJaSlGxEpQ2OZ0ceAZe4eNbMw8IiZ/T6772vu/uUxfI+PAeuBvE6ZU8l+UqkKnnj6CeLxOHfddRezZs1iZGSEaYkqGt56POFjag7qlxzpB3Ot0YtIWTrsjN4zXrl+bzj7GPN1AsxsNnAJcMMRVTgOqXSURDLC8PAwixcvxt1pa2sDwHGqTmnK2S8R7wXQjF5EytKYjrrJLr2sBjqAe919RXbXR8xsjZndaGZTRun+deBTwMEHrx9l7lGSyQgACxYsIBwO09XVRSQQptuiDNz/MiMbuw+6d2wi1gtAKKygF5HyM6YPY909BSwxs0bg12a2CPgO8AUys/svAF8Brt63n5m9Behw91Vm9sZDvYaZXQtcCzB37txxDeJVQ3j2pKdQKMQJJ5zA+vXraZ7RQk/HMNFH24k+2p55vXAAiwRIV8bomX4vzIJIxWi/q0REJq5xHXXj7r1m9gCwfN+1eTP7PpDrg9ZzgLdmP6itBOrN7Kfu/r4c3/t64HqA1tbWI7yE5BCJeOYom6GhIVpbW9mwYQMjIyMEg0Fm/POZJHYP0rfjOXqGH6TPniIaXAeWIphooKZ5/pG9rIhICTts0JtZM5DIhnwVcAHwH2Y2w913Zpu9DVh7YF93/wzwmez3eSPwiVwhf7QEgwniicxqVDQaZcmSJUyZMoW+vg4WnBRkU9vn2bPnAWLJXRCG2tqFHNt0LdOa3kh9/RJd50ZEytJYkm0G8GPLXNYxANzi7neY2U1mtoTM0s1LwAcBzGwmcIO7j3q4Zb7E41U0NAxRVVXJwMBm2to2c+aZjzI4tBpIsXt3LVOnnsO0po8xtelcKiumF7pEEZGCs1K80UZra6uvXLly3P3++Md34Kzeb1tNzXyams6jqemNNDb8KYFA5ChVKSJSOsxslbu35tpXVmsVixZ9gufWvo90ejrzjn0/s2f/OVVVs4pdlohIUZVV0Le0vJ7zl71Q7DJEREpKWV29UkREDqagFxEpcwp6EZEyp6AXESlzCnoRkTKnoBcRKXMKehGRMqegFxEpcyV5CQQz6wS25fElpgFdefz+habxlL5yG5PGU3qOdffmXDtKMujzzcxWjnZNiIlI4yl95TYmjWdi0dKNiEiZU9CLiJS5yRr01xe7gKNM4yl95TYmjWcCmZRr9CIik8lkndGLiEwaCnoRkTI3qYLezE4zs8fN7Dkz+62Z1e+z7zNmtsXMNprZm4tZ51iZ2RIze8LMVpvZSjNbmt0eMbMfZsf5bPbG7CXvEOMJm9mPs+NZb2afKXatY3GI8bw3u+2VRzp7/+WSNtp4svtOzf7bWpd9nyqLWetYHOL9mWdmw/u8P98tdq2vmbtPmgfwFHBe9vnVwBeyz08GngUqgOOAF4Bgsesdw3juAf48+/xi4IHs8w8DP8w+bwFWAYFi1/saxvMe4BfZ59VkbkY/r9j1Hul4DmizGHix2LW+xvcnBKwBTst+3TTB//3MA9YWu76j+ZhUM3pgAfBQ9vm9wDuyzy8jEyQxd98KbAGW5uhfahx45a+SBqA9+/xk4A8A7t4B9AIT4WSQ0cbjQI2ZhYAqIA70F768cRttPPv6S+DnBavotRltPBcBa9z9WQB33+PuqSLUN15jeX/KQ7F/0xT4N/hjwGXZ5/8bGMg+/xbwvn3a/QB4Z7HrHcN4FgLbgZeBHWROgQa4FvhvMjOt48gE/TuKXe9rGE8Y+AXQCQwC1xa71tcyngPavAAsKnatr/H9+XvgJuBu4GngU8Wu9TWOZ1725+wZ4EHgDcWu9bU+yurm4ABmdh8wPceufyKzXPMNM/ss8BsyM0MAy9G+JI47Pcx4zgc+7u63mtlfkPkFdQFwI5kf4pVkrhn0GJAsTMWHdoTjWQqkgJnAFOBhM7vP3V8sUNmjOsLxvNL3TGDI3dcWpNgxOMLxhIA/A84AhoA/mNkqd/9Dgcoe1RGOZycw1933mNmfAv9jZqe4+0T4KzKnSXscvZmdCPzU3Ze+8uGeu/97dt/dwOfc/fFi1ng4ZtYHNLq7m5kBfe5en6PdY8Bfu/vzBS9yHEYbj5l9G3jC3W/KtrsRuMvdbylmvYdzuPfHzL4GdLr7vxWtyHE4xPtzObDc3f8q2+5fgBF3v66I5R7WOP79PAB8wt1XFrrGo2VSrdGbWUv2vwHgn4FXPk3/DXC5mVWY2XHAfODJ4lQ5Lu3Aednny4DNAGZWbWY12ecXAslSD/msnOMh8+f1MsuoAc4CNhShvvEabTyv/Ay+i8yS1EQx2njuBk7N/tyFsm0m7M+bmTWbWTD7/HgyeVD0vx5fi7JbujmMvzSzD2ef3wb8EMDd15nZLWR+OJPAh31ifJh0DfCf2X9cI2TW5iFzpM3dZpYms/Z4RZHqG6/RxvNtMu/VWjLLbD909zXFKXFcRhsPwLlAWyksP41DzvG4e4+ZfZXMUW0O3OnuvytemWM22vtzLvCvZpYks2T4N+7eXaQaj4pJu3QjIjJZTKqlGxGRyUhBLyJS5hT0IiJlTkEvIlLmFPQiImVOQS8iUuYU9CIiZe7/AzhFh+Hlaq1+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - YES for TN)\n",
    "for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>NAME</th>\n",
       "      <th>VTD</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PREIWES</th>\n",
       "      <th>G20PREIBLA</th>\n",
       "      <th>...</th>\n",
       "      <th>G20USSDBRA</th>\n",
       "      <th>G20USSIHOO</th>\n",
       "      <th>G20USSISTA</th>\n",
       "      <th>G20USSIMCL</th>\n",
       "      <th>G20USSIJAM</th>\n",
       "      <th>G20USSIHIL</th>\n",
       "      <th>G20USSIGRU</th>\n",
       "      <th>G20USSIHEN</th>\n",
       "      <th>G20USSIFAP</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47</td>\n",
       "      <td>033</td>\n",
       "      <td>8</td>\n",
       "      <td>1611</td>\n",
       "      <td>467</td>\n",
       "      <td>93</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON Z ((-89.05690 35.81323 0.00000, -89.05...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>033</td>\n",
       "      <td>6</td>\n",
       "      <td>1612</td>\n",
       "      <td>473</td>\n",
       "      <td>102</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON Z ((-89.02185 35.85440 0.00000, -89.02...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>035</td>\n",
       "      <td>Mayland</td>\n",
       "      <td>1714</td>\n",
       "      <td>1028</td>\n",
       "      <td>226</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>206</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON Z ((-85.23575 36.10575 0.00000, -85.23...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>009</td>\n",
       "      <td>Martin Luther King</td>\n",
       "      <td>0411</td>\n",
       "      <td>394</td>\n",
       "      <td>642</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>585</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON Z ((-83.96293 35.78866 0.00000, -83.96...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>009</td>\n",
       "      <td>Everett</td>\n",
       "      <td>0413</td>\n",
       "      <td>836</td>\n",
       "      <td>508</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>469</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON Z ((-83.95053 35.78375 0.00000, -83.95...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP                NAME   VTD  G20PRERTRU  G20PREDBID  \\\n",
       "0      47      033                   8  1611         467          93   \n",
       "1      47      033                   6  1612         473         102   \n",
       "2      47      035             Mayland  1714        1028         226   \n",
       "3      47      009  Martin Luther King  0411         394         642   \n",
       "4      47      009             Everett  0413         836         508   \n",
       "\n",
       "   G20PRELJOR  G20PREGHAW  G20PREIWES  G20PREIBLA  ...  G20USSDBRA  \\\n",
       "0           5           0           1           0  ...          76   \n",
       "1           2           0           1           1  ...          89   \n",
       "2           5           3           1           1  ...         206   \n",
       "3           9           1           2           0  ...         585   \n",
       "4          34           5           4           7  ...         469   \n",
       "\n",
       "   G20USSIHOO  G20USSISTA  G20USSIMCL  G20USSIJAM  G20USSIHIL  G20USSIGRU  \\\n",
       "0           1           1           2           0           0           1   \n",
       "1           0           1           0           0           0           0   \n",
       "2           5           0          10           1           0           2   \n",
       "3           5           3           2           1           7           4   \n",
       "4           6           0           6           1          13           5   \n",
       "\n",
       "   G20USSIHEN  G20USSIFAP                                           geometry  \n",
       "0           2           1  POLYGON Z ((-89.05690 35.81323 0.00000, -89.05...  \n",
       "1           1           0  POLYGON Z ((-89.02185 35.85440 0.00000, -89.02...  \n",
       "2           3           1  POLYGON Z ((-85.23575 36.10575 0.00000, -85.23...  \n",
       "3           8           5  POLYGON Z ((-83.96293 35.78866 0.00000, -83.96...  \n",
       "4          11           7  POLYGON Z ((-83.95053 35.78375 0.00000, -83.95...  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/tn_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1961 0.6182777037206635 2996186 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#see NJ code for convex-hulling tracts\n",
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        for geom in vtdGeom[p].geoms:    \n",
    "            xs, ys = geom.exterior.xy\n",
    "            plt.plot(xs,ys)\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "isSkippedTract = [0]*nTracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5520a7c2-0560-421c-b5ef-bfd8aa9752b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "isSkippedTract = [0]*nTracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later\n",
    "lakeTracts = [0]\n",
    "minTractPop = 20\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        if y < 999 and x > -999 : #optional to exclude some areas from plotting\n",
    "            plt.plot(x2,y2,c=\"purple\")\n",
    "            plt.text(x, y,m, fontsize=9)\n",
    "        #plt.scatter(x,y)\n",
    "        if y > 9999:  #for Tennessee, these ALL appear to be interior\n",
    "            if lakeTracts == [0]:\n",
    "                lakeTracts = [m]\n",
    "                isSkippedTract[m] = 1\n",
    "            else:\n",
    "                if y > 34:  #m==99940 or m==999909: \n",
    "                    thisTract = \"interior\"\n",
    "                else:\n",
    "                    lakeTracts.append(m)\n",
    "                    isSkippedTract[m] = 1\n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dca817fc-8a1a-4037-bb00-3f0e2e871491",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic TN map\n",
    "tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-82.1,35.9)\n",
    "pt2 = Point(-81.5,35.9)\n",
    "pt3 = Point(-81.5,36.8)\n",
    "pt4 = Point(-83.0,36.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  93 tracts w pop 358865.0  to reassign to tracts...\n",
      "[386, 393, 922, 923, 1234, 1319, 1560, 1604, 1610, 1613]\n",
      "I have flagged  91 precincts to reassign to precincts...\n",
      "[636, 1817, 1822, 1823, 1833, 1839, 1842, 1874, 1897, 1910, 1913, 1915, 1943, 1946, 1947, 1949]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1: #and tractGeom[t].centroid.x > -76.4 and tractGeom[t].centroid.y > 38.4:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1:# and vtdGeom[p].centroid.x > -76.4 and vtdGeom[p].centroid.y > 38.4:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  358865.0 people (VAP of 291213.0 ) and  164483.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cut Pop would be  767005.0 which is  0.9988727845477428 of a district\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, ***SPECIAL FOR TN**** completely CUT OUT a Memphis district\n",
    "\n",
    "#plotPoly(tractMAP)\n",
    "pt1 = Point(-90.35,34.95)\n",
    "pt2 = Point(-89.575,34.95)\n",
    "pt3 = Point(-90.2,35.6)\n",
    "#pt4 = Point(-77.5,40)\n",
    "clipPoly = Polygon([pt1,pt2,pt3])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "plotPoly(clipPoly)\n",
    "cutPop = 0.\n",
    "for t in range(nTracts):\n",
    "    if tractGeom[t].centroid.intersects(clipPoly) :\n",
    "        plotPoly(tractGeom[t])\n",
    "        cutPop += tractPop[t]\n",
    "print(\"cut Pop would be \",cutPop, \"which is \", 9*cutPop/np.sum(tractPop),\"of a district\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5ed3aeb1-bd4e-4282-83d1-6af29983db59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cut VTDPop would be  297032.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#does this look like precinct edge?  sure, close enough\n",
    "#plotPoly(tractMAP)\n",
    "plotPoly(clipPoly)\n",
    "cutVTDPop = 0.\n",
    "for p in range(nPrecincts):\n",
    "    if vtdGeom[p].centroid.intersects(clipPoly) :\n",
    "        plotPoly(vtdGeom[p])\n",
    "        cutVTDPop += vtdTrump[p]+vtdBiden[p]\n",
    "print(\"cut VTDPop would be \",cutVTDPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  216  CUT tracts w pop 767005.0\n",
      "I have flagged  140 precincts to CUT\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "#tractReceivers = [-88888]\n",
    "#precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "# no tract receivers code here anymore\n",
    "\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "# no precinct receivers\n",
    "\n",
    "print(\"I have flagged \",len(cutTractList),\" CUT tracts w pop\",popToCut)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to CUT\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  767005.0 people (VAP of 583777.0 ) and  297032.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut.  DO NOT REDISTRIBUTE (special for Memphis)\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K6uxavWyrKfBEf6nR5pCNoh8TiNdTghAi2v+3BTgXOIRmk5jtbzYPyGlnmOs4Re0kpdwrpUyUUmZLKbOBYmCilLKsKzcRogfY9rIWUT3rFyBE98YSAoYuhIOfajEZfcz69ev59NNPyc7O7tCIfSpWq5Vzzz2XI0eOsGPHDvILnsFgiCEt7dpW25uM8cTFzebYsZe/U95PO4rqABidFrgrdWq0hZI6R6teZSH6nkCin1KA14QQOjTB8q6U8lMhRB3wLyGEHnACdwAIIVKBF6WUi/2vw9A8m37YA/MP0QV8TW5qlxzBXWIjYnY61pmpiDOFgeqBYedDRFJwLjrmKti/FI5+qY3bRzidTlavXs2IESO46qqr2s3f1BpTp07l0KFDrFv3X8aM/ZqBA+47zYh9JkMG/47NWxaTm/s4I0b8pbvT75d8uqeEzXk1KAJMBh1LdxwnKdLEuPTogMdIi7Zgc/uod3iIDjP23GRDdIkOBYWUcg/QIhxXSrkezeX1zOMlwOJTXtuBdl1K/LuKEL2Aq6iBmrcO4rN5MaaEU/9pHvXL8hF6hbDxCcRcNgRKdmlqInsNSNn9HQXA4HMhOhNW/wWGLNRSe/QSUkpKSkpQFIVVq1bh8/mYOnVqp4UEaPEUl1xyCStXvomUZtLT21fLhYcPIiP9ZoqOvUxGxvexWod19Tb6JfuO1/OT/+4kwqRHUQQur4+kSDMv3TzlRHLAQEiM1KrhVTa6QoKiH9L/8imE6DEaVh+jYWUhuigTiXeNw5ASjn1nBZ5SG03rjmPbXKYJivdu0TpEpARHSADojTDnt/DhnVC0AbLPDs64HbB//36WLFly2rG4uDiysrK6PKbLVUhsXD6qbwEGQ2SH7bOz76L4+JvkFzzF6FFPtty9fYv5cOdxTHqFb347j0hz12tehxk0e4bD893yEPu2EBIU3xGkKmlYXoAw60j6yXiUMO1LHT5RUy2pDi/2beV4Ku0Y9BakMQJx64rgTmLYYkBA4cZeERRr167lq6++OvE6JiaGefPmMWbMmG6Nu3vPowihY+LEXwXU3mCIJjvrLg7tfQO16V3GTb+mW9fvT+RX2RgQH94tIQEn80I154kK0b8ICYrvCEIRoIB1RuoJIXEqYRMScR6sofzx7cSGp2P25SOdHpQAy04EhCUaEkdA0cYgDno6LpeLpqYmli9fTk5ODmPHjuXcc8/FarV2SdV0JpWVh1CULfh8s4iPHxBwv+zsu/j6P0nk1cWRnNxAUnbHO5FvAwXVNoYkRnR7HLdPqyxo0ofSz/VHQu/KdwRfkxtUcB5qPaOpeVA0iXdprqIN9ktRsON5/+HgTyRjGhRv1eI0QMso+82TQUlD7vP5eOmll/j3v/9NTk4OGRkZXHzxxURGRgZFSADs2vUoIBg/7ped6ieEgqpq6pWPn9hJWV59UObT13hVicnQ/f+t26sJCmMAgY8hep/QjuI7grdaS7jmKW07OlofZyHhznHAOOTL94C7B5K0DZwN21+B3K9gyHnw5YOw8SmwVcCCPwU8jJSS/Px/UVu3hejoyezdU8nBgx5cLiuJiYlYLBauvfbaE1lfg0FtbSGI9Xg9k0lOHtnp/qoPotOqkZ50Pv7XLi78yVhSh8QEbX59gcWgwxGEetcnBEVoR9EvCQmK7wj6eC2fjnFA+yoPU1Yk0tEEeJDmHijfOewCsCbBludhwDmw/TXteG3gKchttjwOHvoN9fXbAair20xEJGRlD8BkvJPLL7+8RwzGO3Y+ihAqo0f/otN9VZ8Pjz2CmNEVzL58Ih/9cycf/XMX0clhJGZHMuKsFFIGRmkqwm8R8VZTUNKD25ur4nUiSC9E7xES398RhF89YB7WdirtZrxH9iCERKR0ftXcIXojTPieFk+x+21wN2opPioOBNRdVT3s2n0r9fXbCQsbzORJ7zNu7AuoqkJUpJ4rrriiR4REU1M5Pt8qXK4xZGS08ArvkPraCpA6rNFmwqNMXPbziYw8O5WIODO5Oyr44O87ePW331C0v/9l2G2PwYlWciuauh0o1+TSBEV4gOVTQ/QuoXflO4Lwb+mlR+2wre/IZgyAbvjkDtt2iQk3aHUuPrkHTJEw5QfwzRPgcYDB0mqXxsb9hIcPpr5+N07nMUaPevK0/EouVxqK0nNR39u3/x2dzsOgQfd1qX9dpZZ0ICJWM/xaIozMvl6LqXA7veTvrmLHikJWvLCPK38zmZjkYHoR9ByDEq3Y3D7KGpykRLX+3gWCzeXFYtCFvJ76KaEdRT/g8W2P86s1v+Jg9cEeu4ZQBOgEeDsWFFTkIjGiG9DNrLFtETsQxt+g/T14PqSOB6lC5aEWTaX0UVz8Flu2Xsyhww9SW7cJEMTGnn1GOyMCV49M1+msw+n6DIdjGIMGntOlMRqqNSeCqFbSmRvNeoZNS+bCn4xDZ1D4/Nm9uByBJ9TrSwYnWAE4WtHUQcv2aXL5OhWgF6J3Cb0z/YCvir6iqLGIZQXLmJk2k9vH3M6kpM6rNzpCGJSAdhSgItEFzVOoVS58Qqu/PXTRyeJI5Qda1OQ+cuSPFB9/A4DSUi1wLiJiFAbDmXmEzCDqemSq27b/A73eRUbGj7s8RkNNAxBFdGLbKVEiYs0sumM0H/1zF+/+eQsjZqYy+pw0zOHdi1HoSQYlajufoxVNzBrS9ezONpe3U0kEQ/QuoR1FP0ARCmennc1PJ/yUg9UH+f7y73PTsptYV7wuqNcRBgUZyI5Cp9NW+D2J3ghTb4foDIgdAHpLCzuFw1HM8ZK3SUm5ijmzD5yo+ZCY0DJXlBAmhHAHfZpuj52mpg+w27MYMXxxxx3aoKnGAcJHVFz7CZJTh8Rw9tVDaKhysvmjPD5/Zk+Xr9kbJFhNRJr15FZ2b0dhc3mDsqOw2+0cPHgQtzv4n4XvMiFB0U8IN4Rz+9jbWX7Fcn479beU2cq4a9VdLC9Y3mr74sZiviz8ErUTD3Sh73hH4frfY5hK3wLRi5tNRQcJw6D89LoNBYXPAgoDB9yDTmdi1MjHmTxpCVlZLfNLCswoPSAodu54CoPBTmbGD7tlJG9+m0oLDnfYdvTsNBb8YBTj5mdQmltP5bFGfD61X2ZWFUIQG26koZuqsqYgCApVVXnllVd45513WL9+fbfGCnE6IdVTP6DOVUeUUVOlWPQWrh9xPVcMvYJzl5zLb9b+hv1V+7lhxA0kh2ur0dXHVvObdb/B5rGRGZGJUWfE5XOxeMBiLhl0CRmRrRfiEToF6WtfUBgOPoEQPlyj78cc1LvsgMSRkHuyvrTTWUJp6fukpl6N2ZwCgBA6oqImttpdKBZEkI3ZXq+L2rq38XpSGD37ym6NddbFcynavY2Vr+zmxgcHYmjDaA/aw3fI5CSSsiPZveoY7/1tGwJByuAossfGM+KsFIzm/vPV1esUfGr3hJjN7SUxonufuOLi4hNFpTZv3sxZZ52F2dyrn+L/s4R2FH2MV/VS56ojznK6kdOkM/H6+a+zIGsBrx94nYXvL+Tur+7md+t/x91f3U1mRCY/nfBTsiKzSI9IJ9Wayn/2/IfFHyxu2ygewILYq2TjsYzFfOVPgnB3nSBpJDSVg01zD9V2E5Cd9aOAuhsM4SiKitcbvCDBPXtexGBoICnploBKpbZHdHwK0y4Px16VzKr/vhdQH0uEEb1BQfVK4tLCKT5Uy/p3c3jh3rV88PgOjm6v6NacgoVeEXg6WIB0hK2bxuyysjJefvllAK666ipcLhfbtm3r1pxCnKT/LEu+o9S56gCINbeMbxgQNYBHZz/KPU338N6R91ias5QaZw2XDb6M+6fdj1l/+mpp9bHV3P3V3RxvOs6IuBEtLyaADhZ+0hCO8NZ1OO/j7z2ELNxE7I3PEZbU9UysJ0j2J+or3YUzfQQlJUtITb0Kszk1oO4WczQuNzQ0VBAbm9nt6aiqj/KK1/D54ph9zs3dHg9gwux55O16i9xNKRwatYHhk89qt73BpOOmv5yF3qjDYNRhq3ex9n9HKM2tpySnjrLceqKTLMSndz/XUlfx+FQKq+1MH9huJYEOaeqGMdtms7F0qVZsc9GiRYwaNYodO3awceNGpk2bhsHQf50Bvi2EdhR9TLVDW0GfuaM4lTRrGvdMvIcvr/ySFVes4OGZD7cQEgDDYjS//HpXG3mEhNDqS7SHTg9qx7r+yN3Pkd64gapnLg2O7rzZ26lkB3n5/wIgO+vOgLuHWzX1VE1N4BHe7bH/wJsYjdXExtwQ1DQgF/zgYkyRNax7uxSft2NVmcVqxGDUHqDhUSbO/+EYbn30bL7/15moquT44bqgza0r7C9pwOHxMSW740DO9rC5vF0Otvvkk0+orq7me9/7HtOnTwfg7LPPxmazsWvXrm7NK4RGSFD0MdVOTVC0tqM4E4POQKq17RV2tDkaOLlLaY2Onulq5HD03kKkvW0vlqqdXxKhs+NQTWSKPEo++1f7gwaCOQrih9JQtpbS0vfJyPh+wLsJgOgoLZNrXX1+t6cipaS4+HmczigmTw5uYUZzeASj51pw22I4ntexYbvNcazaKtkThDxL3WFbgRYfMiW76zmrVFVid3dN9eTz+cjNzWX8+PEMGjToxPHs7GzS09P55ptv8PlCNS66S4fvjBDCDKwFTP7270kpHxRCjAeeA8yAF7hLSrnljL7DgHdOOTQQeEBK+YQQ4jHgIsAN5AK3SCnrun1H3zJO7CjM3du6g2YIN+vMbQoKIUSHqiclawyiXMWddwTj6NYNx/Yv/oJPCsQP11D73AKitjyKeu6tKOZups5Om0QeKzEYYhiQ3bmYhdjYQeTlQ1NTUffmAOTkfIzRWIZefxtGY/CNoYkZmUA1NeXFZA4d3aUxdHoFvUHB2dS3Nci3FtSQFRd2okJdV7D58zxZOykoysrKeO655wBISUk57ZwQglmzZvH222+zb98+xo0bd9p5l9NJY2U5qs+HUBSEEOgNRgxmM6rqw+N0nvg5vPk44bHZJA/wf0eFVt9FSu23qkqklEgVVJ/2t+qT/jbNf2teWdpvebKf/2fo1GQSMvtOhdgRgbwzLmCelLJJCGEA1gshlgEPAw9JKZcJIRYDjwJzTu0opTwMjAfw19w+DnzgP70S+K2U0iuE+BvwW+DX3b+lbxc1Tm1FFmvp3ta9mWhzNLXO2tZPCjrcUigJmseUmr8bWhEU9tJcUm07KIuYTFraCCpn/I6MLb+m8u37SLjlpS7N2dNYSGPRMqqUHVRHwKCEK9DrO/eliYhIR0pwOku7NIdTOZr7HIqwcNaMu7s9VmtExEQC1djqu/eQj04Oo7adbMA9jZSSbQW1zBnWveSRNpe24u/sjqLZWD1q1KhWi1ENGTKExMREVq5cSU1NDS6X60S9kpycHAw1FZjLA1tY6C3noDcHN6WNogiEIlB9KlXFTVxyb4uK0/2GQGpmS6BZD2Hw/0j/T/MSMgoo6WCo+UCulLLQP+4Xp5zbBHTP//BbSo2zBoNiIMIQvNWEbGvbEIDXk378Ofg+i0XsfxcuuqXF+bJXf0i2UAm/4EEA0s//IYUbnyE5/yO8tr+jDw9cBeGpz2PbhoXYTZrHjAiXJDhjSc+8NeAxmlEUIz5fOB539zyBioo2YzIdAS7BYumZFZ4mKMDRTUERmxpOyZG6IMyoa+RV2ai2ubuldoJTEgJ2wpjd1NTE9u1a9uCrrrqq1TaKojB37lzeffdd1qxZg9FoxGQyYTKZALBERnLRjff7dwMqXrcbj8uFolMwmMwYzBYMJhPv/ekP6I1OrvrtZG2dJUEoWlocIQRCOfnQb/03J//WCRQhTssSvPGDXHauLMJp8/TbKPyARLh/N7AdGAw8LaXcLIS4F1ghhPg7mq2jfRcOuBZ4u41zt3K6iurUa98B3AGQmdl9b5b+xvs57+NRPUHJeOryuaiwV5yIt2hBAKonYTDiTVyAsfw9pKoiTknjkb/8ZbKdW6mMm0XSyFn+IQXGub/AtPZuij9+jPTrHgl4vhUH/oXdpJLNeKLjZxE18Gr07dhgOkLKaFTZveyr+w88gU6nY+qUn3VrnPbI2btb+6ObQY0xSeEc2VyOx+07YfDuTU7YJwZ0bzfcXM8irBPG7KVLlyKlZMGCBe22GzFiBPfffz+Kopxwcc47eIDX33mXwSNHM2RqR48tUPRhWMJ9JGb1TFXCgeMT2LGikIK9VQyfntJxhz4gIGO2lNInpRwPpANThRCjgTuB+6SUGcB9QJt6ByGEEbgYWNLKud+h2TjeauPaz0spJ0spJyckdD2XTH+lTQ+lLrCxZCOqVJmY2LptIRDVk/R60Vd+hRTm04REU0UJEWsfwEkYCbe+elqf5Dk3YveZIG91p+ZbWbsWs0swcM4S4sbe2y0hAaDTxaMo9V32wqqoOIpevw2YQXR0erfm0h57tq8FIGXokG6N4/OpIDR7RV+wtaCW2HAjA+O7l+m2OWNsZ4L2SktLGTduHGed1fGD3mAwnBYHk3dISxUzYOiwgK6lN4bhaOq5ioSJWRFYY0zk7azssWt0l059wvzG5tXAIuBmYKn/1BJgajtdzwd2SCnLTz0ohLgZuBC4QfbH/AQ9jJQSi97CvIx5QRnvgw3vEEUEUxKntN5AiI69nurr0MkKPPGnr9RKXrqNeGMjvkV/R7HGnz6solAXNoQY51FkgB4mXnsFNcZ6EvVDTxNI3cFoTMZotGG327vUf+fOf6IoKuPG/jwo82kLQ5imF6841rV5NlNfbscaY0Lpo9Tc2wpqmJwV0+3dcJh/N9SsggoEg8HQ5QVByfHjICWDRwZWb8Uam4KrqaLHUqgIRTBgXAJFB2rwuPqnh1aH31AhRIIQItr/twU4FziEZpOY7W82D8hpZ5jrOEPtJIRYhGa8vlhK2b1vzLeU3LpcHF4HszNmd9y4A+oaalhn38TsmknYPi/SamTDaR9uoR1odxzh0gzhauLJ7LWO8nwGOzdQHjmViBk3tNpPDphNuM5F3b6vAppvU9HnSEUQkzA3oPaBYLGkotd7qK3tyFzWkoaGKiRf4/WOIilpbNDm1BqW2EOguCnJrevWOBVFjSRmdk0d4nQ62bZtGx999BGvvvoqH3/8cacEbEWjk4Jqe7fjJwBSoy0YdQo5FY1ttvH5fFRXV9PU1ITH46GhoYHY2K5du7q2Fr30YQkLbCcUl56FVBuoPt5zRaUGjo/H51EpOtA/C1cFohRMAV7z2ykU4F0p5adCiDrgX0IIPeDEb0cQQqQCL0opF/tfhwHnAWc6pD+F5nK70r8i2SSlDCxfw/8RNpRsAGBGyoxuj/XJxvfxKF4WGeZg21iKbUsZpoFRuHLrsZ6dRvTiAQEZs0VEvFbbofJk1lJXwXYsAjxDL26zX9i4C+DQ09j3ryRm3HkdXqepWvOktqbN73hSAWINz6CxEerq8klP75xaZ/v2pzEYXAwc0PVU4oHQ1FSOJawcJbyChtKuq1KdNg/1FQ6Gz+i8Ttvj8fDyyy9TUVGBxWIhNjaWXbt2cfjwYRYtWsSIESNwOByEh4e3mWp+W4G2oOiufQK0OtlDkqwcKGlo9bzD4eCll16iqqrqtOPR0dFdup7N7cFqNgXcPnXIIHI2QcGeHOLT4zvu0AVSh0RjCteTt6uSQRN6oARxNwnE62kP0MJvS0q5HmhRNEFKWQIsPuW1HWgRJCClHNzZyf5fY3PZZrIjs0mxdt+A9UnJ52Sracy86zJ8lQ5sW8txHKgGVdK0rpiwsfGBGbMjYnCHjUWp2nXimKeqAAB9fHab/aKHTsel6pHHd7XZRnXWU7HrT1gTZ1Jevxa9XmKKGx/wPXZEZFQ2pWXQ2MlYCo/Hjd3xMTolhezs9o2j3eXIkU8RApQIL54SIw67C0tY4A+tZnat1O4xc2TnH9RfffUVFRUVXH311YwYMQIhBGVlZXz44Ye8//77J9plZWVxww03YDQaW4yxJb8Gs0FhVGpwDLyjUiNZdVBT75ypysrNzaWqqorZs2djsVgoKCggNTWVsWM7v/Pzej14FT0xkWfWM2mb7HHDWPMGlObkAt1f1LWGolMYMDae/N1V+HwqOl3/ioUO5XrqI1SpsrNiJwuyuv9gOlpwiIPKUX4cexuKoqAkhRN94UCiLxyIr8lNxVO7qHr9AEqYHkXR3O98Li+Hn9uLrHcx4v6pKKcYRNWIoRgd72tqKiGQtVpaDEPy0DbnIHQ66pQkLI1HW79fVyN7l0+hKtIHBUvBDHGOiKDZJwDCwzUDtNfbOaPgrt1vYjbXERfbvVTigVBa9il6vZGh40ZxoMRGzu4ixs7o3O6nJKeWXV8eY+CEhE554thsNg4fPszGjRuZNGkSI0/R0ScnJ3PHHXeQk5NDQUEBbreb7du3s2LFCi666KIWY31ztIop2bEYgvRAG5kSybvbiimpd5IWfXpm3ZKSEhRFYdasWej1+hNpOrpCTXk5CIE1MnDX57j0FIRiovpY++lhfB4vSx55hrrSfCIT0zn7mkvJHDWo3T6nMnB8Aoc2llFyuI6MLiwAepL+Jba+Q3xd9DWN7kamJLdheO4E7299B0UKLp3e0p9cZzUSd/MopNOHt8yOt9xGQ2EDOX/cTGRpE1F2D0UrCk60l6oPXe1uJGZtBwLIpgo8qkJYfPteSc64scSKajy1pwe92cu+YePqyZqQAFJdqQwKW8jIs97t5p2fjsWsuQWrvpqA+0gpKS97A4/HyujRwUn+19Z11q37OWbzHnTKbEZN1oRD3v7AAwSdNg+bP8njk6f2YIkwMHlxdsB9S0pK+Oc//8nHH39MXFxcq26liqIwbNgwFi5cyEUXXcTMmTPZvn07+/efXiekrN5JTkUTs4YETw0za6imhvtgR/Fpx2tqatiyZQuDBw8OSs6tmiptERHZiR2FEAKzNZnG6uJ22739h39y/MAX2BtKKD28hvf++CuKDxcEfJ2MEbHoTTpyd/U/76eQoOgiywuWM+a1Mfxhwx84Wtv6KrotvKqXZ3Y/Q3ZkNguzF7bbtqKmhId+fRWv/fcvrZ6vqihnaeOnzBSTSU5Ka7WNMSWcpHsmaOkE7F6qn9mNyePDMSkJG+BdfxyfU/M4UcuPY/Dsx5N0yirSXoPDZ8DcgfHPMHIRQkDtptMFQP62e3ArHkZbr2X+vFxGnL+O7OnPYIwJzD0xUPR6TYWjysA9R44e/RpLWBHW8ItP9D+Vw4c/Z9u257o1L6/Xxdq1t+H2fIjDPpnZc/5FYlosUu+h8ljgleG+ev0g2z4rQKcXXPbziSRkBLYqllKyYsUKDAYD1113HbfffvuJoLP2mDdvHmlpaXzyySfU1590D12Xoz3Izh4cPHf1QQlWZg2J541NhaelLM/NzcXr9bJo0aKgXKfOb+eIiu5ckGBkQjpuR4XmktwKhzcdpPzoWhIGTOe+t/7Hlf/vn0jgg78+gtsZWC13vVFH1shYjm4rx2X39KtCVSFB0QU8qocHv9Eik9/PeZ/LPr6MYw3HAu7/4t4XOVJ7hLsn3I1eaX+VVFh8GGuBg6qPvmHZitdbnP/zij/gVNzcM+cX7Y5TV1POmvIPOVC3AadOYL15FEOuGopuVhpmCQVLNWGnJKYjpe60YDDhqsOFuUM1Ufz0y/GoCu4jJz2f7EUrKbfUkKYfR9LUP7fbv7s0G15lJ/zxc44+g89nYMKEn7Z6vvj43dQ3PNblOfl8Hr5YeSle3xqaGueycOGbGPwCyRwDzprA5mqrd5G/p4qMETFc/otJRMa3XvjI5/Oxf/9+VqxYwbJly1i7di0ffvghhYWFzJs3j2HDhgVczEen03H55ZejqipLly5FVbWH5MbcauKtRoYnBzdy/ZaZ2ZQ3uPh878ldVllZGSaTiZiY7kV/N9NQpxnho+I6l1stPjMbpIvSI63vKtb+9y0QOi752Z0IIcgaM4ipl/wAt72EJX96MuDrDJyQgMvu5cWfreO5H6/mf3/cgi+Q8sU9TEhQdIED1Qewe+08cvYjJ3YEx5oCExT7q/bz3O7nWDxgMQsCMJyWlhec+Hvvq+/w7HO/we7SVqEfbVjCl+p6bo28jmFZbfuE532xkf8+8HPKHUc41LCHoX+YTsJI7Ysy6PwBNOgE6p5KvC4fuJoQwgf+TLQAOncjHiWsw7kawyOpF3Hoq7WsqNLnJXf//QgVsiY+2mH/7tJsX2gzhckZVFUdxWDYhWAm4eEtV8dud/fzKO3Z8xIm0xEE13HRRc+fVhshLj0M4TFTmNOxO+/uVcdAwswrhxCb0nJnJ6Xk4MGDPP300yxZsoStW7eya9cuvvrqKw4cOMDo0aOZNKmF70mHxMXFsXjxYgoLC1m9ejUARyoaGZkaFfT4jTlDExmUEM6zq3NR/cK+uLiYtLS0oNmOGhs0z6q4xKRO9UsfrqkKC/cdanHu4Dd7aCjfTdqI2UQlnhRAs647n8SBsyjLWcOG975o0a81Bk1KZM4Nwzjr8sGkDYum+nhTv4itCBmzu8CKghWYdCbmZMxhdsZs1havZVn+Ms5KbT9K1O1z89v1vyXeEs/90+4P6FpFR7Uo0gse+SPvvPAIuq/38cSGa9CnJrN08B5GMpi7LmiZbqKxqIK1L7yMT3rJPboVqzkGZASKzoTulFQJQhGEz0xDt7aYos/yyV6QggCEOBn8ZPDW4zRnBzRfT/QQYmq20FCwjH2HforDpDJQmYopqntRyD3B7t1PIoRk1Kh7Wj2fl3dyZySlihCdW1e5PXbKyv+DqsaxcMGDLVxNpy0axtJdu/n0mV3Mv9nN8PHZrY7j86qaoADi0qwtznu9Xj744AP2799PQkIC11xzDcOGDUNRFDweD3q9vlsP2vHjx1NQUMDatWuJSUhi3/EGhiS2nEd3URTB3fOGcO87u/jiQDme/K2Ul5dz3nkdu1sHSnOsSHQndxTZ4zRHjk3vPcnxwwex1dZhjY2hoaqcutJDCF0Yi3/cMjfaVb+/h+fvymXTe/8he9wwUoe0X+RLp1MYNUtTIUskxw7Wouj6JqDyVEKCogvUOGuIt8QTYdS23hcMvIBPcj8h2hRNnDmOOEscseZYLHoL+6v3o0qVnNoc3D43+fX5PDHnCaJMgRnTaouLEWGS4YMm8OBfl7B89X/Z8ME7ROWXMzIsjgduegyD6XT3xdqcYt774/+jwaXpY1NiB3PJww/w+u//jNfV0lc9Y0EWR9cWI3eUI85PREqB9GqrmKbCPUTq7NQHGoSWPAY7W9mZ9xMwwiDdDLJmtVSZ9TUuVxMe7ypUdTgpKa3fW1nZ2hN/FxV9Q1bWrE5dY+OGhzAaG4iN+RN6fctkb6lZiYy7KIFdn1Tw5YuHGP5Udqvj6PQK4+ZnsHNlEXUVdqITT+7ubDYb77//Pnl5ecyfP5+zzjrrtHQVwaru1ryr+Oj9JYQxlnpH5116A+HCsSn8a1UOz6/czdj67YwZM6ZbXk5n4vX5QFXRd/L/Ygo/eb/H9q4EoMavhYpIGMvcm28gMj66RT9zmJlLf/lblvzxF7z/yCP88LknMQZgHwItZTnQL1xlQ4KiC2RGZLIsfxn1rnqiTFHcPuZ2tpVt49X9r3bYNzU8lXPSzwn4WrK8AXHK6m3RnOuZN/lS/n3b1dxguIDs7NPDUSr35vL+3x7A5XNw0fd/rflnz5uGTq9HoIBsqe9U9Aq+YbFEHK6h5tPVxAmJSNPy99esfhkrEDHt2oDmqx8wgZ1JmhBM1c2jKfseqpoaSIiMDvieu4oQAlUVKErHqSB27PgPBoOTlOS2M9Xa7btRdGYMBid5eZ+dEBT19SVs3fYwNtsuQJKZ8WMmTLjptL6bNv8bt2cpHs8oxo9v+383a/F49nz9McLXMlbhVMbNz2DP18V8/K9dXPbziVhjTOzdu5dly5bhcrm45JJLmDCh59JUG41G5s6dy9KlS5lqrcEU27EqsivodQo/njuYzz9cQq2wMGfOnG7XKz8VrYhR543EBqOJsKhoHA31/PDZd3C7HHz496eYsHAh489rX5BljhrAlIt/wNaPnuadP/yDG/786zYDGU+l2dYW2lF8S4kzx6FKldy6XCYmTSTVmspHl36EV/XS6G6k0d1InauOOlcdadY0wgxhJFoSyW/IJ8GSgEEX2GqmrqkGSyOYx53uzWS0hpEYmcWR/RuZUX4jYUmaoa9kyz4+eOKPqKhc8bM/kD7l9JWyUBRkK4ICIOuigVQdqsa732/UTtYy9ZoKVlHviyB69JyA5hwxcg74a9qX+L6C3K8o9Joocc1i0ojvM2HQ9B6NVZBSh8/XvpeJqqrU1L6HEHEMH35Zq228Xjd6QyFSnYHbfRCjcQkrvvgAn8+IyWRHCDAYItDr7VRW/Yny8rNIStKE9vGSLdhsT+B2RzNn9ovt3q+qqvgceiIS2n8YhkeZuODHY/noiZ1s+noX+dV7KCkpIS0tjUsuuYTExJ6P5o3zq2uyvcWoZcVs2DeEs0YHHicQKJeOT+Xh9zJokBb+YAhu4ShVVQNJUNAq0ckp2Ovr0JshPCaOW/7+YMB9z7n+fI4d2E9Zzmpe/cVDzL35BrLGDG5XYKg+iRCclpK8r+j7Pc23DJvHxgt7X2Bw9GDGxJ8slqIIBaPOSJwljuyobMYnjmdOxhyGxAwhzZqGQWdgaMxQYsyBe2/sOvANCoKMQSNanDvnlttweBpZ+vsHOLpsPZ/85k+88/j9CEXh6t890kJIACg6HaraumEsLN5Ck0mP0ePPNRORgOpxE+M7TkPU6BMxFR2hhJ18YJW45+Kx3k8js0g3r6G26EaWLD+XdXveaXMe3UVKPV6vo902hw59gtlcQXT0FW1+UQsL16LTeYmNnYbZPBMARVExmew4HDFYLD9i4YLtDBn8JDqdj8NHtHpcNlsV+/f9E4DRo14kPLz9B3hxQRk6r5nErI51/unDYrDHHOWbvcux2Wxccskl3Hbbbb0iJADS0tL4xS9+wTU33IwqFD745PMTnlDBRK9TSPOrcX751jdBHbum5Dh0cc6JA7SFQOHuHV3qf93DPyN91AJqj+9g6SM/41833cRr9z7Ajpc/wWNztmiv+lSUfqB2gtCOolPUu+r55ZpfUuWo4q+z/hrwzqCr5BzZCcDoES23tllnTWRewe189dELfPTqX1GEjqGDZzDrh7cQmdl6PQpFp0O294BODEMp1fzlRUQidfu+JlbxoWQHlraguKyGm59aydjsBYyIqOW2K5/361dvo6apmhVbXwd1Ce6q+3l3+XNkZv+es0Z2nDlXSkneFw9QXvEprsgmwptSGTz2fmLHtIxBUVULqtp2cjmAgoKX0OlNTJxwZ5ttSkrWAJA94Dxiom8j5+hchg29AI/HjdFoOrFLyMo6l0OHY3C73mTTJjf1Df9Dr7fjcY8nK6tjVdDh7ZqReuDYjtO4CCHwmWyE6aO5++6fBCUArbNYrVZGDLGSNHwy1Ye28O6qLVzbgeqlK0wfmsLBygK+LAxuqVeft+vjZY4ex67ln1C0bw9Dp5/d6f6KonDNAz+lLO8KdixbTdG+HVSV7mBtjWTzRgOJhhrGLRrE4IunaXP1SUQ/UDtBSFAEzCe5n3D/es1T6ffTf8/k5OCWRWyNytyjKAbJwMzWXV/HX38xyaOGUV9cRtrU0VgT2vfksERGU1dqw+v1ode3VHXoY0yIMk2/L0xm3PlbATAN7FhQfLl+J/d9fBQHZi5UFnH71RefpnKJtcZx3dz7cLh/zLKtb6B3/Yfyop9RlvoFydEtV8RSSur3rebYvheoFbvwJLowmI2EN6TQFFvMzpK7SNoymZE3vopispzSLxJk27UDysoPYDQdABZgbqfGd2PTDiCahHhtFTliuBaAaDKdrgpRFD1W68W43a9hs7+M251ISsqDjBl9RYf/M4Dc7dVIvcKQsRkBtTcbLbjcjj4REqdyx+Xn8cBf9rBj29agCgqvT8Xp9fG/LSfdzY+WVDM4tfs15QESswZSUtu12hJZY8YDUFGQ1605JA9MY/GPb0D1XsM/r7+ENFGONbyCPGc67k9yTwgK1SfRhQTFt4t/bP8HAH8++89cPKjtLKrBwmFrxHSgBke2FaUdY17yuGEkjwsswjk2NYnSw17KjpaSPrxlYR7p8gEepBQIvcBXcQhVQsTglkLxwMEc3lu7n6QII0cqbHxYasSK5OUrBnLOtJb1i5uxGI1cPvM29uSMpKLwRvZ8fC1J31vVQo+/+7+XUp2yDxLAXBlJUuMChlz6KIrBiKMqn32rb6N8wDbql57F6MlPEzVEc01WRBQobScF3LvnXwgFxo5tPcAOQFV96HR5qGpgxuFpU3/B9u0WzOZ4zpl1HYYA9eqbv9yHr85MynilVcHdGhZzGE3OuoDa9iQmo4GIlAG4jx/keGUdaQnR3R7T5fEx4Y8rsfsr3ikCVAlf7s4PmqAwGPRIRbSafLAjjGYzOoOBuvLu12UHcFRoeaeSByQw6w838fpt72I0nNzxqz7ZLwzZELJRBMyz5z4LgOiyKaxzfPrCEwDExgevNGLacM3wWLD7cKvnvZUOvGoGQkh8+btQ6oto8lkwRZ5MUGa32/ntE//jklf28XK+gb/skbxfFsYQk4PlvzqvXSHRjJSS0gdfJfJTHbr0Qo58fPpDu/bwKqpT9hFZnM600Z8w87qdDLvkCRSD5hlkiR/A5CtWkW27FldkI9uPfo9Db/8IKSU6XRx6Q1Or6Q/s9lpUuRaPezSJCcPbnF9x8Wb0ejfR0YHl4TIaw5gx45dMmHBLwELC7faw9eNjSKOLC24KPCOp1RqBT7hw2AJLC9GTzJ42AUXAp+u2B2W8T/eUnhASAKt/Pgs9PrbmVbXTq3MYDAYQCh5X1/5/1phYnI2tp0PvLE0lxwGwREUD4BJmTvWcVX1qnxWlOpOQoAiQoTFDiTRGsq18W1DHbcsYOPfGHwAg41pP1dAVBk4YBUBpzpFWz8tGNz60RG9qzg70jkociubqun1fDr956gPO+sMnvF0WwbBwD+/cNJq3bxrD6rsmsOyha0iJD8xQ/+Vj/yFzx1ryfdcRXpxAccTnbH1jLpXb3sfndnBky+8RLhi94BWsya2r3YQQDLroz0wZ9wGW+niOJ62kaNmjGI3J6HRebLaKFn127nwOvd7NwIG3tzu/Y8VaoF1W5rkB3U9X2PTFXoTbxNgFyZg7kWY8JSUZBOzf0fp72JvMHDMYNzqOHz/e5TFqbC6cHh8/+e8Ofr5k94njf7tiDJnxkaSavRysbGno7SrNcSV2e9ei7s3JcUgpefhHF/PHX17Nfz/7N2obnoQdYS8rAyAsLh5VVfHoLFgsJx/J0idDxuxvG4pQmJg0ka1lW7s9Vl7VDr7Ke4cNpVvY11BFisnM8+e/Q1LEwBNt4mKT8AlJff6xLm2TWyM8KhKdMYaakoIW5w7n1rBF/Ygr9csA0E09H922h9nvzeb8X79DjbACRgbobPxqdjzXLVzcpTl5fSr6996mKGkgVzzxIDjuY//Ht1KVuJs9Db+C1b+CNEiuPgdL8sAOx4tIH8OUK75m44fTyDO/QJjnSpoUqK4+itV6Mk2Dz+ejvn4pkMSQIee3O2ZDw3aktJKSMrrT9xco1RWannzM1LZTt7fG9DkTWL9tFVs372DyrI53bz2Joih49FY8jXWd7ru3uI7XNhTy3o6WuZNe/v4U5g3X7FYjEsysPObD5nARbul+kJ/BX1vD2dQEsZ1XZ9WkKLAHhM2DcPkofX0FD25Zx133/ZOU6NaTcrZF0zFNRRqWkIirtgkpdJitJ+NpfCHV07eTKUlTONZ4jHJbeceNT6HBUckn+5/kVysu5dz/jueSz27mXwc/52hTNZOikznmdPD9z66kvPGkkUwoCq6x8ZiO1LF0+fNBuwdrTBr2+pY61t//bw+LDe8SqRThGnE/W1e/QbTewQ79CBL0Hu4YovLFj8bz9V9u5PpF07osuHYvW01yQwWmK65Cr1PQW6MZd/1SZk5bS5btCqwVSQz03MzIK18OeEy9IYyxk59H6qFJWQJoVe5O5cCB9zCZa4iLv7pd33VNKB8FObhn4z38Gha9vnNfwfAIC8lR2ZQ3FlJVFng69Z7CaI1C527fy+xM3tlaxEVPfXNCSIQbdcSEGbh91gAevmTUCSEBMHVwIioKa/fltzVcp2hOiGhrCjxr76ksmHcdAJmLZvHr/3xAxMyRRB2y8fzPb2fZhnc6NdaB97Usy2GJidhLNLd0S9RJ1eW3ykYhhDALIbYIIXYLIfYLIR7yHx8vhNgkhNglhNgmhJjaSt9h/vPNPw1CiHv952KFECuFEDn+38FJD9mDNHs6Bap+cnoa+dEn53HOu3O5f9sLfFl+lCSzlduHzOHtBU+y+rpdPHvRSv467V7K3e4WwuKG638FQHVdSzVKV4lNy0T11tJQdfLL7Xb72NbQRAyNbE27iX1mKzNy/s6usBlcf+cfWPHnG7n/tosYmt25FVNrHP9sJR5Fx4TvnR7oZo5OY/BFjzLtxg0MWPhApx/SUQNmMDj87hOva7ct90fhahQdexWPx8KE8Xe0O055+W4MBjuRkT3r1SZ92v0pXfCwPm/xXEDlrVeW4PP2bcK4cGsEZjydiqd4Y6NWAOiuOYP48Mdnsf/hRex8YAG/u2AkN83IPq3t/HHarvKbg11Xb52KxaJFlNsaOyfcmhmQOgwVSV11BQajkTt++ihn3/cTDKrCvidf57F//Rinu/04nmYGT9RsYIXr1tBUVqfNL/ZkwkctjuJbIigAFzBPSjkOGA8sEkJMBx4FHpJSjgce8L8+DSnlYSnleH+bSYAd+MB/+jfAKinlEGCV/3W/ZljMMCIMEQGpn9xeJ3d+dhHf1JSxMHkI/5xxHxuu28hbl6/np2f9m9Epc0+sbBcM+0GrwqKuXjPiRUREB+0ekgYMACTH9p+soVFS0kiYcGASXpS6fMbs+D17jeMZde8HJKdnBu3aAGG7t3I8fShRMYEXjgmUzHPuYbT1r+iPmoleupVt8+ZTtmMHx4/vwGw+gtFwLkZj+6knCgu/BCAjo+P4ju7QVO1GVTyEWTsfeTxweDpjBk2j1lXKk397nvqarj30gkkgKSmaKa13YtIr/GrRcMZntL8+zE6OJVLnYffx4BiQw61aYKOtqWv/M5PRjNsssdfVnTg2ffr5/PiJ15DDElA2FPK3+65hy65VHY414S+PYvSpHNu/B0+9Nh9j1CmCQu0/NooOZyE1mvdpBv+P9P80O6JHAR3lSp4P5Eopm+sJXgK85v/7NeDSwKfdN+gUHROTJrK9vH0vD5/q5b7ll7Ctvpo7hszhbws/4Nyht2I2tJ2/vzVhUevfSURHBa+SWOpQbYVWln+yrGNVnZNYoX1QJ9nWUSNiGPTj91vEDHSXhvIq0mqO4xk7MajjnkrS1Ks4+9bdGM6/G1N9HSV3/JC9mx9HVRXGj7+3w/519VvxeMykp/fsjsJe50Uf5uuyeuvy7y1i3JAZ1LsreOOFJUGeXefobH0dj0/tVAnVwdE68hrEaTvErmKN1B5ZXVU9AfjC9HgaTu8fFRXHrx56lewbLsDQ4GXdX/7Jo/dfTU3RwTbHUXQ6ksIiKKqtomKP9kzRR52M7flWqZ4AhBA6IcQuoAJYKaXcDNwLPCaEOAb8HfhtB8NcC7x9yuskKWUpgP93q3kIhBB3+FVb2yor+75E4OSkyRQ0FFBpb30uqqry25VXsra6hOuzJnP3Wf8OeOwzhcWxCs2zJaaVgLSuEp2sCZ0VW46yfodmq6hudBPPySCkgmE/ICyIwqmZ3HXaTixmas8+hHV6PaN+8hMi//Y3zE1NxK3ais87gdjY7A77SvUwPu+goCaiO5XDuwsoOHIcX6ORiKSuR/YLIbjshoUMTBlFlb2IPZtbd3nuaRx2O24RuE/M9S9sosHpxeHxBVzBbUp2DDZpYE9u99VPEX5XVLut67VGZLgBYXO3eu6Ki+/kB0++iHtCLOTbePVXP2fdY3fjaqhttf05P7obBdh4eBXuxg8wRJ1M5fJtUz0hpfT51UfpwFQhxGjgTuA+KWUGcB/wUlv9hRBG4GKg00sfKeXzUsrJUsrJCQnBK73YVTqyU/xp9fdYVpbLxanD+fU5bf5L2uRUYfH5oU8BiI8OXiyFwax98PJUO7e+s4OC4w28s6HwxI4CYPSFdwXteqdSUay5A2YObT8nf7AYuHAhjgmS8DUKqSkXdNi+uvoIBmMjVmvPZGF956kv+fLZPD77x2EEChPmdOzV1RGXXX8+CgY+/Pw9dnyzv+MOQcbZVIfXEHhtig25mtHWp0oKqu0B9Zk/Vvu8rNxT2EHLjon0V8pz2AO7dmvozCaEu22bTGJMKrfPmcLS2cdR4urZsi2fl++6nt2v/QXVe3pm4+Szzua8S64GQPXmY3ef3Kl863YUzUgp64DVwCLgZmCp/9QSoIUx+xTOB3ZIKU91FyoXQqQA+H8Hz2LbgwyPHU64IZxtZS0FxT/W/Yglx/YwPyGTP85/p1N621NpFhZGr/Yh2fz6n1j1399xYPO7NNV1Pip0+Su/4KOnfqiN9ZmWsE7qrfiAy5/8hq9qG/i5XvPA2B82FWtE8O0HAML/5TRGBreEZlv4fD7Ij8CdJSmvXN9h+/wCrQpZWtqcoM6jrrqJN/66kqp9CqZ4N0q0jZghKqOmdD/zakRkOJdddBVSqnztr0DXm+jdTZisgX1eXJ7TVUfhxsB2bZOHpGEWXrYWdN/LyxodDap6ooBRV9CFmdG5298NRY+4nHi9k9en1HPeDWcTE6by5eff8OaPLqF4zfuntR3xve8zYtolAKfVllG/TXEUQogEwCOlrBNCWIBzgb+h2SRmowmOeUBOO8Ncx+lqJ4CP0YTNX/2/P+rs5PsCvaJnQuKEFjuK5zf/klfyvmF6TCJ/X/hBl4VEMwuG/QDPfBt765dTdaSOsj272cVu4HWMESqRyRZi01NIHjic9CHTSMqcgKLT3s5dq1+ivOAg+1cc1DLvq/4SoT+WFB88QJ0+kmGzkpniTOOFA8cZHWYhISwMmsA3uyMNYtdRXFrglLAEL4iwPY58+BFhdU0Uz09AVddhs1UTHq75ztts1ezd+wI1tRsBD0JYUdUy9HojWVntVyoMFFujgxVvbaVktwukQmS2l+t/fi56Q3DDl8ZMHsr2zcMpqNhPQ20TkTHBrz7XGvklVZjwEBUf2E6/eTcBYDXpSYwMzAamKAqDI+FIbfcz1SqKDkV2T1AYwyyoHtqNb9JHpfHgqB9y65GXqctK5ppnPyPnnb+yetk63nnmFc7ZupwpP3/+RFZmg79ei954Uh3Zn3YUgXxiU4DXhBA6tB3Iu1LKT4UQdcC/hBB6wAncASCESAVelFIu9r8OA84DfnjGuH8F3hVC3AYUAVcF4X56hclJk3ni+BNUOaqIt8Tz351/5KlDyxgfGcNTiz9Br2u/CE2gXHDOPVxwzj2oqkptxRGOHdlAee4Bqo4VU1/aSNXRfI58XQAsRzGoWBN0OBskJ3evp3/Iju75mJoCB8fNAxhuUfjVNeP5mXs0RqMep+NrjuQdZuyonjM0E6WtPBuLS0hOCU7unvao+t/bRBkMZF7zG4qr7mHLlsfIzl7IkSMvIpRt6HReIBIpzSjKMcxmOw7HaAyG4FRve+3Br5F2M/oYF+dcPYKRE4Jfu6GZUWOHUbBqP7s2H+KcRT2fsBJg7U7NUDt+xOAOWmp8sFOLm7h8Qhq/v7DtGu+tMTEjkn377BwuKmdYZufqXZ+JUQjsrq5He5vDrDiloMleT0R4dJvtLP4qllL1IHR6hl7//xhwYRXL//QT1m4txfbQ9zjn/pdQjCa8Hk0lpT9zR9FPUnh0KCiklHuAFkpbKeV6NJfXM4+XAItPeW0HWjwVpJTVaJ5Q3zqmJGv+z9vLt+O0HeJve95hWLiV5xZ/gkkf/MpfiqIQlzycuOThcEpxPJejnuKjGyk5uoOKglxqj1fhbjq5vU8eFc7oORdQtH8DOeuK+PiRFwCFvNiBzI3SVvVGf/1ssyWcoT0pJICBc2fiev5xDr/zIUOm9GxUcUV+PtEHDuI5eyZjx15I3vKnMRqXkJe/BKHo8fkmkp31fQYPPu/E7q+urhiLJXjhPKpLR1iqm1sfuDBoY7bFqIlD+OxLQUFeAefQO4IiNy8fn1SYOaZtQXGkrIGr/rOJ354/nAmZMXy8u5TyBicx4Z1bTM0bk8nr+w7xxa68bgsKa5iZmiY7UlURXdj5W6yROIGauop2BYXXX/5WeE7uXgyR8Vz4l7f46k+3s/1ABfk/uozZV12Dz6PNw2A6+UjuT8bsUAqPLjAibgQWvYVX9zzLodqjZJnNvHjBR4Sbont1HiZLFIPGLGLQmEUnjlWXH0JvMBEVO+DEsXHn3MSKsF+y7/ODkAQFlkwy44Pv1dQRAyeM4JMhE0hdsZSqe+8gPr17X/j2yH/9dSJ9PlJvuw2AaVOfZtu2RwgLy2bs2NuJjGx57ejolhl1u4NEoutk5HVXCQu3YBYRVNZ2LmtAd3DWloElFmM7qrQvD1VQ7/Dwm6V7+fEcbUc1LLnzNqqZo7IxsJ9NedXc3XHzdklOSaUqv5Di3BwyhgSWeflUwq2R1AK19RVkpbWdgiUr82zY+Sg7i9bgsFcxffxtREVnI3Q65j3wEpnv/YN1n6zgg1dP+vgYWqie+oeNon/M4luGQTEwzBrDvtpckkwGXrrgPaIsPffQ6wxxScNPExLNLLz5Ma5/9EHcc64BIRiZ3n2Pm64w/Pe/xuR1880Df+3R68gtW7DFxhA7Rdv9xcYOZMGCFzn77P/XqpD4v4DFHI7LHbwEeu3R5HARptqJTWzfI29g/MkAsqdX56JTBD9f0PmHs0GvY4DVx8Gq7hcyGjFuPAB7t2zuUn9rhLbrrG+obrddTPQARqo6XrTl8IvCD3n2i5MiTgjBkKt+zs3/eY+5s/0VLIUJk7l/2ihCgqKTlB6tY/V/DzNOKWSgycez814iwZrd19MKiJSsKRyqcBFnqSchqnfKZ57JkKnjyJ06n4Ebv+Do9n09dh1dfQPe+PgezdfUMRJk713foDfik8GtCNcWxyo0D6ToqNaLP3l9Km9tKuCut04vG/r7C0YQbuqaImNCmpUar5FjlXVd6t/M0FGjQUoKCrqWPyryhKDo2Avr4ZkPM1/R/ke59rIW53WWSCbe9RjTJ12EOeoWDKc4emiR2f1DUIRUTwEgVUnhvmp2rCikNLcevUnH4JE/YsKQf2NoEm2ECvZPDlWaGRwXWC6anmLGH39L8flrKPj7vxn89n965BqqXg+e3nlo9heio2KoaCzkWF4pGQODF3vTGuXVWoBmzBmC4uaXt2DSK3x1qAKvetKF9OfnDeVHswdiCLBAU2ucMzKNdw7ns2L7UX7QDYO9wWAgwmigpqkB1edrtzBYa0T5g1FtAWTNHTb0Yp4YejG/eHMWhzwtK+uVVhZhdzupUj3oFf1p/if9qWZ2/5hFPyZvZyX/+9MWPntmD021LmZdM5RbHzub8XO0coU1le15BfcvCiuLKLfFMCkz+Ab3zpCYmULx8IlEHem5HYVMTMBQGbyCN53leH4FwmfEZO29r9icBZpb76pl63r8WlV1WoBmXPRJe0NZvZM1Ryr54kD5aUIiyqLnx3MHdUtIAMwdNwg9PjYc7X6GhsyMdLxGC0WHD3S6b6x/N25vCjz/VJwhgkJFcqz60GnHb9y2m5lH7Pxw2tlsz7LiajqpOgypnr4FuB1evnzlAMv+sxeAc28ZyQ1/nM7YuekYjDoSU7UKaU0N3auf25t8vW8nALOHt13drbcwhIejBCF3T1voBg0mrKkJR03fpOJe9to2QDLrkp6raXEmqVmJxJhTKSrP6fGsslKL0EF3iteQx59B1qhXCDdpQkEA4UZ9t+OKAMLMJjIsXvZVdL+639hJk0EIdm/Z0um+zYLCYQs8X9TFo28G4KEVPzqZukRK9ptP1kr/eJqV7G2HSVm1k4yVO/nTAivrLX2bHbiZkKBohdKjdfzvT1s4sqWMKRdkc83vpjBsWjK6U7aBYREx+FyROJzdTyvQW2zIrSRM72DSoLF9PRV8egN66e24YRcJy9bSPtQc7v0cSJXl1TjLDMQMVEjL7l3D+cCBg1AVD8cLezbRgd6vrnF7Tr6HVr+rtapKJvizwkogPiI4cSkAY1PCqHDrqazveq4mgMHDRyCkSmFR57+/YaZwPDoVlz1wQTFq1DX8zjqSzZ5qPt2gOXIcKzn9s3ljXS3Xu01c4TFxvseI16hQlxncxJxdJWSjOAWfT2XbZwVsX1ZARJyZy385ieSBbacnkJ4UvPJYp65RsX0d3gonqeef193pdgq318uGwigmp9ee+JL3JT6dDoPagzuKyCgk4OtGltCusuPrHAQ6xs9t6X3W00T601Q31nbvQdoRcVGayqm67qTe3WLQPldeVbL+aBXhRh1CCP5w8aigXXfWsGQ+yjvOih1HuXHuuC6Po9PpiAmzUFffgNfjQW/oXIJGrxGkvXO2vqsueIEP/zuLpw//lwum/4p79hxAZ8rijSQXc4ZPa2ErGbZuL4awrieODCahHYUfj8vHB3/fwbbPCxg2I4Vr/t/UdoUEgF6kgyHw3EuF77+Hewmoa8w0HuldldWXezfT5AnjgtH9wzXUp+jR96CgEH59+JlJ2HqDgl21SIOLEZN6J/nhqegUbe3n68H/LcDIAakAHC87aS94fl0e6dEWHrxIi7qeOTiefQ8tZGJm8IIYz504GAWV9Ye7Hy8ycMAAVKOJnF07Om58BqpRwefonCuyzhzJ/LhxHNfBqxuWssE8kEctJcwbdVarBnWdgP6heAoJihPsWFFIeX4D5902kvk3jcBo7nizZTJloTfX4bC19GY4k+LPP0a3NQlPuPbFqv5yV3en3Ck+3H4Qs87JhZPmnDj25Os7Oe//raCqvPdX3camepo6KCLUHYRee/9kLwsKu82Bp85IdJYuKHr5zqIo2gPHp/bsfcdHWXFioL72ZPrsf63KobjOwV8+1wy2EQF8hzpLdISVVJObvWVdz9XUzPjpMwDYu6P9+jKtIU06VGfrqcbbIzkyA1WJ4i/OZM5y5nH9tLazGuuEQO1ssY8eIiQogIZqBztXFjFkShJDpyQH3C8iUgtaqyw90m47n8eDd5MeV2Ixmb+9CNeYI+iLEqjatjngnPzdodFex/qCKM7KaiDcorkzfrkmn38cKCHH6+WrNQU9PoczCasqoyqi56LDTwgKT+8KirxDxxAIMob0jc+04q8N4ZM9vxb1Ga24Gk8GnTV/lt0+FYNO8LsLRvTIdcckmSlx6qm3d8+onZaRiU6qHCvpqOZaS4TJAM7AP1s7qovJ/vJL7rZn0xh7M07FxGNjR7ebQkQvBN6QoOg/bFyaiwBmXNa5pG1xSdoXobJkT7vtGvMPoHdHYJkYjU5vIPG8c1B1DpzvuSn424ccW/4hjpqWwTjB4vV1K7F7w7hu6kgO7C7no48O8Zvlh0j3rz4PlganzGRnMFeX407oOTWY4hcUqrd3YylqyrTdWVxK64FoPU1zFThdL7hVpg8YQrivic0H8rG7vKgShiZZmTMsgQ/umklsePCM2KcyY3AiEsFXu7qnvhVCkBAZQZMKrk5mk1UsJoQ7cGG85NgenLp4nGFTcIdN4UfefQxqJ/0HaA9nX/+QEyFBUZJTx9HtFUxYmEVEbOc8DFIzx+Bzh1NX34GLncH/pfXXl4hIHELifePxzTqOVH2I1XFUPXqY/H+9S9mGL6kr2IcvSMFiqqryxhZti/y7d2pY/PY27tmYS5NUeWzxSAYbDByu690APKfDSUxTLca0tB67huI3Ttobe7eedElOHRKVzD7YUaiqyt69uwGIjev57LxXzJ+BKgUr1mxkW6GmghqfEc2rt0xldFrP1DQBOHfCYEDyzeHO7wTOZOjQoUidnr1bNnaqn8HScU2KUxliPblwCPeU8cu5N3TYRy8Evn6yo/hOez1JVbJ+SQ7WGBMTFmR2ur+i04FrFF5d+4FjEZkjaNB9jfv4SZ9+c3wiWRdci1wsacg/SM2WnYhDEXg/NtFELTVRH5H6o9mYY7pX1e/Xb62kzBYNQIpRz30T0hkyKJbhA2OwhhsZtrGALTU96yFzKnUNDlZddTMjgbgBGR227yphERE4AXzdr2EQKKqqUpXnQhcpe60mxKl8+fEGyhrzyY4bRdag1B6/XnpiDO7IVGRZDu+u16531uCeTzaZmhBDnN7DruLuqxUnnnU2a7fv5MDevUyeE3gya0NYGIpHoKpqQLaoZwpLQRnCg+kObhwwF6O+492Wrh8Jiu/0juLgxlIqixqZcfkgDAFW2zoTs2koOks5Hlfbq3KdzoBU1Far0AshiBo4kgHX3kDG7xZgvt4As+vQN8ZSumRVl+Z0KgP4CoB/nzOADx6cz3WXjmDymCSs/jTPwxOsVEqVqsqeFxYOt5dl37+bkYV7Obbgcqb/4Loeu5YU2kdb9LD3z6kc2FqAcJvIGhfda9dsxudT2bZzEyZh5aa7rui1695+3WV40SELtqIguXBMz6YOaWZCsok8m47S6o4dSdojOi4Ok1Qp7WQUvzk8HEUKGm11AbUvVoYAcGP2BCICEBIQ8nrqF0gp2fppPskDIxkyueu68oiIgQghqSw92m47oSqIDvTGOoOZ+LHTST//InyjS9HnpdCQd7DLc2uqPMqw1Pf5bPZGLlo8stWVz4iMaAD2H+nZdBc+VfL+Hb9m/IFvqLrqZhY8+Wf0ET236rZXad5lppjguWZ2xJpXCgCYem7vR76vXb4Ft2hi8vjpvZofaFBqPNkTzyZBsTHJVIa+l65946zhqCi8sLzzHktnkhwfi0MoNNbVdtzYjyW8OY6kc7bFCEPg6u3QjqIf0FjtpKnWxbDpKd3KMBodpxmkqiva9nyq2rkJxWdBiQi8WEvKBeeh6p1Uvb8bj7Nrq6aCnc+DVMie8IM224wequmyDxTUdekagSCl5J1fPMKkTZ9TOvdCzn741z12rWZseVpm0IjBgVVfCwZZU7Rgt8M7OxeE2V2klGzethG9tDD3ghm9em2AOy6eTZ0llZEU8/aXnU+J0RVmjx1AssnLRwfq8HbTBXrosOEgFHZ3Iu14c6rx6rqOI+CfOaLZP3Rq5+xlOkFIUPQ1ZfnawzdpQPe8UxJTNUHRWN/6jsJVW4ftg1rcERWkLlgY8LjmqERMi4wYq9Mo/O/STs/Lbaujks+Jds0kPDm7zXZJ6ZHEIDhU0nOeTx888h8mfP4mxeNnMvfpv/VK6m9H7lGkEMQO63ztg66y4MaJSJ2HfeuO99o1AbZ/sw+nrGfs8Cnou5l4rysIIfjj3Tfh1FvZs+4LduT0/P0LIbh+cgrVPhPvrN7VrbHGTp0GUpJ/NPAEn1ERsQDU1LcvKC7+5iMePm4B6SZZdC7vmE4IvP1DTnQsKIQQZiHEFiHEbiHEfiHEQ/7j44UQm4QQu4QQ24QQU9voHy2EeE8IcUgIcVAIMaMz/XuKsrwG9EaFuNTwjhu3Q3hkHD5XBA5nQavnS99ehfAaiLo6HUNY5yp7JZ89F8/oYsxHBuM43rncPUVbX0fVO8ga0vZuAvwFVMJMHK7vfgBTayx/6i2Gvvlvjg0ay7xXn+5S6cmu4Ms5ij02BpO194zKRpOB+MEGPNUmSno419Kp7Ni6GyEVzr3orF675plEhJm57abrUYTkv+8u6bhDELh9wQQsipdXNxZ1a5yI6Bj0qo+q6vYLEZ1Kc2LAuoa2VbbXb/qELe4sRugKOTJrLNvnXdKpeen4dqmeXMA8KeU4YDywSAgxHXgUeEhKOR54wP+6Nf4FLJdSDgfGAc1K90D79wjlefUkZkUGRZ8rPQl41Zapj+sLDqIrisc3toy4IdO6NHb0HG1FXLM18DQDqtdLqe1dLM4hxA+d2WH7sckR5Hi91FYHV1isef5t0p9+hJLUQZzz9ovozD3jV38mUkpMJSX4Mns/hcbMi0YhEKz7pP3YmmDh8/korysixpxCmLVvE8gNzUolLG0oYe46PD2cvRbAYjJw/hArOTYjmw90L6Yi3GjA1ongzKgITWXrsLeuTvqg+DBf2VPRq3aWz7yAyE7YJpr5VqmepEZzjgeD/0f6f5r1NlFAC6dmIUQkcA7wkn8st5Syrnnojvr3FF63j6pjTSQPDE5QlEI8Upy+spBSUr10L6rBQcrCBV0eOzptAs6YQjyHAo9CLdn+KW5LKenJ3wuo/QWT0/EBL3/UdcP5max8/EUS//EwxckDmLHkDcyRna+T3FVq8/Ox2O0YR/S+UTljcBL6SDcVh914eqFw0pG9+fiEmyFdqP3cE2jPNYHSS5UF77twMgqSJ5d3r7ZJhDUcL0rAmRLCwrVnh6eVfE8+1cdPD5Wgky5eGB6DSddxFMIP1/2LMa+NYcxrYxj92jhGvzaWgn1XUlb8fOdupIcIaDkthNAJIXYBFcBKKeVm4F7gMSHEMeDvwG9b6ToQqAReEULsFEK8KIRo1vUE0h8hxB1+1dS2ysruFywBqDzWhKpKkgYEJyhIr09EMdac9iE79t77GCtSYFID5piuB18JIdAN9aGvi8VRHpg643j5G+g9MaSNuzKg9hMmpnJOuIVXj5RTF4Tgu5y9R4l95SkKMoYz66N3iIjtueCr1qjYrBkloydO7NXrNjNsRhKKx8y2tZ0vitNZcg9phvOREzqXVaCncNhtuIXhtJT8PUlGQhQzkhU2VQgKyrpeeyQsLAwUBXuANSaiIjUbhcve0q18e00RHl0MV8baOT+t4zQmP934LBvyXgQgLeE8Rmdcw+iMa4kwJxOj9q5jRFsE9G5KKX1+FVE6MFUIMRq4E7hPSpkB3Id/13AGemAi8KyUcgJgA37jPxdIf6SUz0spJ0spJyckdC/4rJmyvOAYspsxm5LRGW3YG5tQvSoFb7+Dsj0J96ACMi66vNvjx0zS0inXbN3WYdu6vL00WXeRZL4cnS5wVc+9i4fTiOSdT9vPW9URzoZGvFddRJjXxYR/P054ZPdsQF2hcbem9kmePr3Xrw0wfcEopPCxb21xj1+rsqISpCAtq39kBXbbm/Dpe1cF9rPFY/Eh+PvHW7s8hsVfq7qpPjAPw/CwSFQkLkdLde3OOs3WMSbAXfRXOS9hCB/Dmms3s3zxP/jfvPv537z7mZowAh3dL9IUDDol9v1qo9XAIuBmoNkdZwnQmjG6GCj270AA3kMTHATYv0coz68nIs5MeFRwdOZh4VqQUVXeHor+/SH63am4hxaRfet1KAFsOzsiKmMcrqjjuA92vNovPPwiQtWTPf7WTl1j4qRUBuh0fJHbvV3by69vAkDVGUgePrBbY3UVb14u9ogILPE9HyXcGuZwIzGDBO5yC1tW9eyuoq6+DoNiRq/v+yQLbo8Xo6uOsOjgLOgCZdLQDEZHevgi30VNY9d2xHqD5rrudpzeX/X5cLu1XYO98aR6WQiB1wBeR8vr5ft3GcOtgcbwKKRHDiLWdHo2ZYvBgsPTt/XtmwnE6ylBCBHt/9sCnAscQrMpzPY3mwe08C2TUpYBx4QQzQrU+UDzN6fD/j1BVXEjhftrSB0cHbQxI6LSiCqeje5/KkplFJ45+Qy4JThCArQPpX6YwFCdSF3u7jbbuWw1VOtXEuOegzkq8Cy4zcxIimSfw4XP0zVD5MYNRTxe7KY+LBZTat+VW1Vq6/BG9a6660wuvn060uxgy/vF7N7QfjBmV7HbnDR4y4mL6Px73RNs2p+HQagMGND7TgQ/OXc4bqnjHx8FHgtxKs0WlVMtFMdz1/HFpxNZs3o8Kz4dx8at01j9yU0nzvsM4HW0XPHXuLWHe0Z4YPm2FH0kTa6WwX5mnRmH91siKIAU4GshxB5gK5qN4lPgduBxIcRu4BHgDgAhRKoQ4vNT+t8NvOXvP97flrb69yS2OhefPb0Hc5i+05li2yMmYSjJB24BIOL6OAYsugkhgqujTTl3AT6DjZr3D+F1tK5HPbbzTaTORebgzu0mmhmWGokLKMiv63Tf/QcruePjfaTodMQMHou3shCp9o3Hhq6xEdnHgiIiysrFd09E6j2se72QT17egKoGL++Uz+vjrRffQwofU6ZOCtq43WHnQU0gnjW29w3rC6cMJ9vi4oMD9TjcnXci8Li1xJnmMG1VX1+Vz75DtyP0Ttz1EejDmlB9Aq/lG6rKNMP5sDnHSc1suQgodbpAqlj0Hddb2VJViPSUI2XLz4ZFb/n2CAop5R4p5QQp5Vgp5Wgp5cP+4+ullJOklOOklNOklNv9x0uklItP6b/Lb2MYK6W8VEpZ217/nsLj8vHZM3tw2b1c8OOxhEcHz1UzKjYDn/DRFKMSM3p80MY9FWNkDKYFRgw1yRx7++MW51VVpazhfSy2wcQO6ZoWb3i2tlU+lNs5o2BZlY2b39iGScAbt00jcvRIpLMBV27vBp41Y3DYkVF9k+b7VDIHpXDDAzMxxDko2uLklYe/wBuE+hh2m5N//vUZjtceJT1mGJPOHh2E2Xaf0uPFODAxNKP37SVCCH5wViY2Vc+zn3f+UWL32xoio6Oprchl46bF6EweRgz5Nwsv3cS4kR8ybsR7AOzb9heO520gNqORzIGVLTyljrk8IBRiDB1rFB7a/gJSGPjz9HtbnLPoLTh9zl6pWdMR35nI7EP+BIALbhtFfHpwXTUVRcEh9KiOwFN0dIXkWfPwDC1CdzQZ3xnVtaqOrMFlLiYl+souRz4P86fzOFhUx45dpXz0xVEaattf0fi8Kvc8u4lGVeWlS8cyYGAMlvFajWT7lr1dmkd3Ubw+MPZOzEZHxMZH84OHzydhNDjLjLz39Jpuj/nhf1fQ5K1m0rDZ3Hr3NUGYZfdRVRWaqlB6sBhVR1w3bwKJeidvbi/H28mswbYmG0L1YTSb2bL+TvRmN8kRvyBz6AL0BiPxyWNITB+Pp3YAHsMW9u770Ym+FbbTnRaMhmgA1lZ0rHKssFdjMsRxVmLL2hRhhjBUqeJWO19JL9h8ZwRFdYkNU5ierDE9k6ffZ9Fj6ETFq65iGReLohqo3PbNaceLc99A8YaRPqnrGVmjI83ogKfyK7j8fzu456vDXPTYGl7872727i1voTqxNbm567G1bLY5+c3odMZNSwcgfJrmpeXY1/Puoa2hqCrC2D+K0gPodDqu+vFcwtLcVB2CnL2F3Rqv6Hge4boYLrpubp+UW22N/QUlmHGTmpbeZ3PQ6XTcMCmRGo+et9Z0Lq7CFLWTESNWs+LDaeijczE6z2P0tDtbtJs4/TF8LgMGqw2vW1Nv5lacLvxnRWjPgY21dR1eV5VudErrixqLXvPE6g8G7f7xKesF6spsxCSH9VyeoWgTJinxuXtWWCSMnY/XUo/96xqc9VqBeXtlKXWmb4hjAYaw7qWsmB+lubP+eUo2fzt7EKqAP+0p5qK3tjHhd8v50V/XsOSDA2xYW8Dlf1vNF/U27hmewvdvGHtiDENSHCI8DteRQ92aS1eQPh+KlAhD/xEUoKlGLvnhDBA+vn676wK0qrQOp6wnLaX3DcZt4fOpvPHuh3ilYL5/kdBX/HDxNCIVF8+vy++UyiYpcw9xSSUInZtw39WcvfiZ1ttlTGD6jE+xqjcwbMJTAJTVnO62vq5Wi9ZOMHasYZCqC6UjQdEP7BR971PXS9SW28kcGdtj4xsTLCjHm2jIbyRmWM+lttYbzERflUXjG1WUvPoV2T+5mqKdryD1XrJG3NLt8f91z1lICWH+ehVXLR5KYV4d63eUsCW3mhV1TSzfrBnTw4Cn5w1j8YKWGVqNGcNwF/T+jkL1GyX7m6AAiE2MImm0kYq9Kgd35zFiXOfdh7dt2A0CRo/rO6+yU9lxpIh3P1pGmLOKhFEzGDmg5wsmtYfZZOSy4RG8dsDNp1tzuGhq++VGAWpqCjAYnCjKZSy8+LEOF5PRCQOZdu7DeLwuDkuw23NPO1+uRoMOrkjv+D1SfU3oDK3b08w6LR4lJCh6CbfDi73eTXRSx14IXSUsPQK5q5LGgvoeFRQA0SPH0DTnQ4xfp5P/1huUJ7yP1TueqLTuGzUtYaevghRFYcDgWAYMjuV7QJPNTW5+LUfya5kyJons7Nbv1TJ+Aq5DG3AcyMcyckC35xUozSVkha73s6gGwnnXTOLNvZtZ+7/DDBud3elcYweO7EWvWhg9seMHYE+SU1zOK0s+RV93DB0K5qyx3HnleX06p2buu2wG7x78kidXHgxIUOTkaEkMszIv6ZTGYX3Os+gFxEVPOe24Dk1FG2Vof0ehqipedxlRka17ifWnHcV3QvVUW655NMQk91yUcOQgTV/pLO2dsqLpCy/FO6IU04FBWGqGkZXVfpbYYGENNzJudBJXXTS8TSEBYD1Xq4vQsOIbPKXlvTI30NQgQL/R3Z9JdHwEaVN0eGtNfPifztVpPlZYQoOrioyEob1anOhM7C4P/3n5DZS6YvTJQ7nzx3fzm1sv7zf/8+gIKxcMCSOnUc/Xuzo2KNfWrsbliiArq3PZdz0+7QGeGX96BgCzogmbGnfLPFCn8rNvHkGoNiYmjm31vMUQEhS9Sp1fUPTkjiI8KQyPlHireu9NTb/2UlTFTUTZNEqPfUTJ9o8o3vwe+d+8RM66f1Cw+RWq9m3C6+z9D1r4tLFgDKPmP3/m6Nw5FP+yd5IDN7s51geYiqEvuPj752BIbqJ0j4u1H7cdQHkmXy/7BqRg3vl9l04c4O7nPseqNhE3ciYP3Hk96Ym9V0UwUH59+VkY8fGPZe173jmddej0RxBiIrpO7kLNxmgA7K7T05PbpKYyyre1n7b8SI2WhPO8tNbjYEI7il5mzduHAYhKsPTYNRRFwalTEI2958pWvXcDimrEE11KrW41B+t/xmHbr8lzPUKR52lybX9id8UNrF03kQPLH0INgg9/oCgGA4m/ehDLpPPQJQ6i8dPXsW3reXdZr1/15GgltUJ/QafTceMvz4VwO3uWVbJ/W26HfdxuDwWlR4g2JpMxuG/zOqVZtMj9Gxf1TS6tQEiItrIwW8cltnc4sH19m+0OHXofRVFJS1vcZpu2sBj9qcbdp8cduRTN5jA9Prvd/teMuh2AA/WlrY8fEhS9i8epfbB1+p69XV+YHoOr98qh29aV4jXXM+qmPzBj8hrGZb3OhMHvMG30cmaO38zkIR8zNO7PhLtGUGp8ncaS4KURD4S4Gy8m+60nyXr9BYTBQvmfH+/xa0ZEal/S5OT+kdaiLcLCw7j6F2eBzsvXr+XgbCVd9ansWL8fVXiYMHFCL82wbYx4UKUgLqLndujdRkpu0i3nB/plxC5v6ebaTFnZCjweEyOGX9jpS4SbtJgRp6fWf0nJgTqtWkKk7Lj+9sjoDADeP9oygBZCgqLXmXxBNgBqD6eUEFF+F9leiKeozdmFsTwdZYILncGMJSaJ+EEzic2cjDVxCObYeKIyRpEx7lqizFoexvCkvknSZ8pOwTh4HJ7jBT1/rYgIfIqCr7bjL2pfk5ASy+SL0xEeI1+8t6ndtjVVdQAMHJbRCzNrH73egEDSYG9fuPU2ufu2suXVX7P7qyXk/HUmU46/DkCyp4iyXStbtPd4nAhlL1KOxtCFwkJWk5b80O2pY2/tMQZ9+TnzdmqlAO5K7zgzwGi/oCiv+pJye12L8yFB0cuYLJpzl6eHH+CGRC1Oo76g5/XjtV/tR9U5SJ4/v8O2dm8BBmc8enPvp/xuRgm3It09/4HX6fU4IyJQS8t6/FrBYOq5oxAmD0U7GtvNBeX1aTtVo6nv3X6z0lMQArYeyO/rqZxg04v3Mei9c5la8Bzj1v6ABFchKzPvofEnB3BhwrXyjy36HD78EXq9m6SkRV26ZoTFXw7V2cDiHYXY9Wknzn0/e1SH/S16I4pRcydOMLcULCFB0csY/YLC5ehZQRGeoaUGaSxovTxisGgqK8RQkIY6vBqjNbrD9m5ZicHbu6mfz0SxWJCe3smt742NQalqu5Zxf0JRBKZUO8IRzo61h9ts5/OXFjWY+t7td/bEEahSsGln36RoOZP1bzzE9OKXqSCWnbOeZ33Mpdi/9wXn3fowEfFplA+8gizbbkp3nr6rKD7+CT6fnpEjrurSdSOMcfgk7LV58ShWLrCWsWpCMmsmpRFtDGyHoihmIiMnteox1hxH4fT2/c7tuyEozJqgcDt61n4QNSgaAOfxwKpkdZXKLzcAkHBeYN4vXuowaJni+wwlKgp8Lry1DT1/scQkjP3Y6+lUtm3bxjH7XqTBxb4VVbjbWMx4mwVFP0hNkhofjSssAUdJDoeLes/1uTU2fPQCZ+f+g53Wc4j69V4mzL+Gs+95jdRBJ1f0yZc+jBMT7pUPnzjm9bqBnXi9ozCbu5b7Ta/Tky/TkP7HaILRzKjoZIZFBr4oE0KPT219AaVTdJh0ptCOordoVj25e1j1FJ5owS0lvuqee2NdDbXoDsbjySohPDmwNA5epQG9Et1jcwqEsIljAGhYsaHHr6VPTcXkdOLox3YKKSW7d+/ms88+Y8jQQVz202nY6zwnPPTOxOdrFhT9I0b2e1dejILkxdffIq8kOCWKO8vetR8wacdvOWwczZi738FkaT19jTEygcpBV5Jl38Px7csAyMn5DIPBSVJi19ROAHafyoO6J3ld9xMAypydWyDWuprwOI4gWkkx3oxFb8HubVlFr7fpH5+6HsZg0bbrba3WgoUQApdeQTR1Ph9+oGxfsZ5MXzSx88cE1N7n8+DTN2IUfZfVEyDqgtmUPxJG1dNPgpBIpxtfYxPS5SL6sgWYBgYvmZw5KxOAmsOHSeujcqjtUVxczEcffURlZSVpaWlcddVVGI1Gplw4gC2f5JM5Ko5h00732lJVv+feKb7+a9asYffu3cTFxTFt2jQGD26ZSqWnGD0onZFnnceBDSt58fnnUNGhl15c+nBiUjL52U2XYu7B3U/u/q0MXXU7xfp0kn/4PnpT+x5YyZc+jPPx9/B++WfkxEUcO/YRCB0juqh2Agg7I+jxRwM7l1Ylt1GLs0iPart+R3+pSfGdEBQndhQ9LCgAfOEGzA09E0vhcnsx74thS6zC5UMCExTOuhIQEpOpb33vdZFWEn/+ABWPPUD5gz877Vz9R0sZuvbToF3LmpWNA2gqKIA+FhSFhYXk5OTg8Xjwer14PB4OHjxIWFgYl112GaNGjTpRxnTS+dkcO1jD2rcPkz485rRSvV6vF+RJQZGfn8/XX39NcnIy5eXlvPnmm0yZMoX58+djNvdOzerrF85ge1YKSz79Ap1ej95owl1fjbt4Pw88XsaffnEnxgBqMnSF0nWvk4WPmDs+JiquY1doQ0Q85UOuISvndY5t+wxVbkf1DicsrHvBggOVUvJUrRTy9ITOCeqRUVo/k6Ft1VdIUPQixhOqp56PcdDFmDE3uPE0uTFYg1ufYsP6Ioa4JH8cZWSuo5IYS8e6UNtxLZjLEtf3bpVx378E66yJ2PfmoI+MQImyUv3869jWfIT7eBXGtODseqIGDcQB2IqKgjJeV8nPz+e1115DCIHRaESv16PX60lPT+eyyy4jMvJ0TxdFEcz73gjefmgzO1cWcfaVQ06cK6o6dKJep91u59NPPyU6OprbbrsNgFWrVrFp0ya2b99OVFQUl112GZmZmT1+j5OGZzNp+OnFKZ96ZzlVBzfx5rJ13Hrx3KBf89ih7UwsfYeDlomMSQr8HpMu/gOOx9/B9dUfMEyxExG7sNtziVY80MXChWEGI1KYsHvaTvtj1vePcqgdCgohhBlYC5j87d+TUj4ohBgPPAeYAS9wl5RySyv9o4EXgdFoJWlvlVJu9J+7G/iJv/9nUspfBeGeWmDsxR2FKTkcChuoy60nYVzwPI1UVcW0qYyjVoWN8TqKnW5iAgg0r6vcAQJiMjtfLtOZX4+7qJGIc9KClp7dNCgD06CTQstz4SJsaz6k4fOviL/96qBcIyo7m1LAXVISlPG6SmWlprv/yU9+QlxcYHVQopPCGDQxgYPflDL1wgEnHDG8qqbOfOaZZ2hsbMTlcnHDDTdg8GfJXbRoEWPGjGHfvn0cOnSI119/nTvvvDPg6waTO69cwP1/2sXhQ4cgiILC5Xax+fmfck7V/0CA6YK/dKq/ISKOkthpZNZ9Q4E3lpEjr+32nCJ1KnhhjL4QrdJz5xDCgMvXtleTRW/51ng9uYB5UspxaP+JRUKI6cCjwENSyvHAA/7XrfEvYLmUcjgwDjgIIISYC1wCjJVSjgL+3o37aBe9QUEoosfdYwHCM7VtZFNhcL17duwsJbPBx+rBKghBlDGwmIgGx04MriQskSkBX6uuYB+Hn3iGqv/soWFZPuWPb8db1zOurRHzZyCMETSt7X7lt2YUoxFXWBiyj11kExM1P/uams6Vlh07PwO3w8uhjVosiK3eRWL5OYzKmE54eDjDhw/njjvuYMiQIaf1S0tLY+HChdx88814vV4OHuzdSPxmdDoFxRoHjuB6nu1458+cU/U/7NLE+oRrGTxycqfHEMPPxaj6sNYJrOHdF6JWHURTx8pZl3Spv1BMuHxtf7f6i+opkJrZUkrZbM43+H+k/6d57xwFtFi+CSEigXOAl/xjuaWUdf7TdwJ/lVK6/Ocqun4b7SOEwGjR9cqOImpwNBD8LLKN645TZRJ4B2kGsDhTxwWKVJ9Kk/4gESLw9OOq18ue/XdQMei/J455qxyUPbaVpi2t56TpDrowI8JiRW0MrmeHz2hAuHonbqMtUlI04Xz8eOdqhycPiCJpQCR7vj6GVCU7VxYhJcw/fxa33HILl112Gampbdd9iI6OJj4+nsLC7lXS6w56gxEhg6fq9fl8DM59jT3mKYT9oZyzf/yfLmXQDR+qCZeYIHlARugFdmlBtuO51B6KYsLdjiD41ggKACGETgixC6gAVkopNwP3Ao8JIY6h7QZ+20rXgUAl8IoQYqcQ4kUhRPNSeCgwSwixWQixRggxpZX+CCHuEEJsE0Jsa97KdwWTRd/j7rEA5igTLglqTfC2i7l5NQwpc1E8PpZyKYikgXB9x+alptIcfMZ6oiInBnytI18/hsdSTqRuAl5rDa7EQqIuGACqpG7pUarfPIAzt+5Ee0+5jep3D+Mq7nqQoWqvR4nqflGp4kN5rH/nc9xuDzq3hyafSkWTDVVV2XO8FF87kc89gclkIjY2loqKzq+Bxs3PoL7CwSu/+YbdXx5j1DlpxKYGHlmflZVFYWEhbnff1FtWFAWF4KXM2fHV+yRQh3fk5dANNWhc1ll49IJ4GRy1cJReh1uYcHi6FjulUyx4OlA99QdBEZAxW0rpA8b77Q0fCCFGA3cA90kp3xdCXI22azi3lfEnAndLKTcLIf4F/Ab4vf9cDDAdmAK8K4QYKM+oXyilfB54HmDy5Mld/uQZLfoeD7hrxm1UELbgucjmryogQwdT5w3gmT1HSRJ1AfVrqNTqBkfGtV2e0udx05C3n/2592JQY2gK06Jth896kLKGrzHuysA41kTSyMmU/2M7jn3VOPZVI8w6EALp36U5dleS+rtpKGGdd4kUOgPS0b0dWFNVLSXXXE2cy8b6J5JJsdup0ut57OlnkNFxWEuLeEfR4YiM5sc33sCg+J6rdngqRqNR81jqJIMnJeKyezn4TQkZI2KYdc2QjjudwtixY9m+fTsffPABV111VZ/UihCdKEXaHlJKBq+/j0YsjJj/vW5OSuAxm9G5grODjdRpj9BaVwNhxo7zO52JXmc+UdeiNfqLoOjUp8evNloNLAJuBpb6Ty0BprbSpRgo9u9AAN5DExzN55b6VVtb0HwHeszZ32jW94rqCUC1GjF5grN6raqyMTjPxsFhEYRZJPu9iYwIC2xsZ1MxAOHxpycDtNvLeP+l77Hi8/GsXjeCHcevxGUupilsLzqPlWzTzzFFxGMdlYVAoWbHDgxxFpJ+NgnrrDT0CRakT4JXRRdjwjggCnyS6re7ViPbOGAUrqN7O1XjGOD40SI+vfdBVj3/Dhv/9SIRLhsHZpyPXehQhaA4Mwuj24W1VPN+Mqg+Iuuqef7559mY3ztqGSFEp++rud/oc9K46rdTOO+WUeg6qWbJyspi4cKFHDx4kGXLlnVpDt3B0ViHWx+c7LLOugpiRBOHrNOwhHctivpUfHoDijc4aslIfxW7WlfXdtQGnQWf2v+N2YF4PSUAHillnRDCgrZr+BuaTWI2muCYB+Sc2VdKWSaEOCaEGCalPAzMB5oLKX/o77daCDEUMAI9Zn00hxuoq+idCEddrBlTrRN7jYOw2O7VwNizMo+BwPB52Xx8bBcuwrg0KTB56nSUIIQBY1Q8u/97iD2byhgwJJrd+2uAW4jM2kjm+A/wmmoZZP1/ZEy6ASH0J1af0SMmUBf1Pvo1kXinOTDEWYi+YCBccLrgkVJS8vAmXDl1+Bpd6CJaLxbfFubRo3Ad3IC7uApTRmAqgeUPPUHW2/9hEMBy7Vhuxgguf/lxUlbvRu/xcHTeBI6UV/DVvoPcOvts9hQWYfOqrPr4Qz5+602811zLrCGDOjXXzqIoSrvJ/nqSGTNm0NjYyIYNG4iMjGTWrFm9cl1VVdG5GtBFB6d+dtG+dQwDxNTbgzKe1OtRvMHZ8Uf5s87Wuru2IzbqLTS0k2Leorfg9DlRpYoi+i6RRiBXTgG+FkLsAbai2Sg+BW4HHhdC7AYeQVNFIYRIFUJ8fkr/u4G3/P3H+9sCvAwMFELsA/4H3Hym2imYmCMMOHqpqJA5RdMl1x+t69Y4Drub9P117M20MDw9mg8qaoimlnPbqIjVor/nGAZnEp//YTPr15bQ4Fb9QgLCo2ppKJrKuElfMH9eLtlTb0GnM56motDpDVgvSEDnjKBszRdtXkcIQeQ8zeW17LFtqJ7OqfiMgzXB4zzYcQGfZiI/fheAxr88ycHzriIvaxQj/v4Xtm7S1G1egwGzXs/YtFTuXTifSLOJs4cNYeGoYdz4/e8D8OUbr7Fl3dpOzbWzSCmD5lrcFc4991xGjx7NqlWr2LNnT69cs7CsBhMeEpKCE+RZX7ALgAFjZgRlPKnoIEjCO8aoLQTru5gZOUwfhk9tX/UEfZ8YsMMdhZRyD9CiWoqUcj3Q4oklpSwBFp/yehfQwo9NSukGbuzcdLuOxWrAafMiVYlQgvPFtTntmI0mdMrpGT0jsiJwrAdbUSNMDdwt9Uy2fpXPQC/Ez86g1FbFRmcSF4flYdR3vEtx1VdQr9tL7Za7qKhyMjjDysi56TRVOhi6OJvS4h189Gg9m5etZuHNl7c5TvzQ2ZSyEY+jfXff8BkpNG0oxVfrpP7jPHTxJuyb1hI2cQgR505t92FpHqIJCvfRPFjQdiS1rb6Jb559A1lZQaatlsPX/IhLLzuPqZedd6LNP3//Bub0NFyDo2loaCQysqWqYld5E+a8g4ikND5f+SUep4OZ53U/+Ko1+lpQKIrCpZdeSlNTEx9++CEGg4ERI0b06DV3HC4AYEhWcNKyqPYanNJAXGyQYkKErt38Sp0h2hQGOKjroD52W+iEGcVXy3MfP8GxpmOkh6dxw/xbifRnhW4WFHavnTBD3xWK+k5EZgNYrEakKnHZvZit3c9Bc6y8lGs+uYaZ4XN57LqHTjsXNSgaB+Au77qqS/X4iN5WxYEEA+eNSOSvez/FSwZ3DGy9EPup1Hx0BZ7Kw9Qf/j4VjcPISDCz4P4ppz2w0gdMIW7gy+RuSaV8bh5Jma0XNdLkuebG1x6KXkfSzydR8oeN2LZq/v++Bj21r9yCYeAEwqfOoOGz99CnDSRizlwiF52DebiW1NA0RPvtOd56DYniw/ns/OX/I6I4nwz7yUR/gxafXoujuqaRL5wRjCs6yuahUzmw/xDTZ2jOdA6Pj8c35/PfjYVkF+xkvq2OcWdfw5ZNm1i5fgMOh51zL76s3XvsKn0pKAD0ej3XXnstb775JkuWLOG6665rEYMRTPKKNNvYpBEDgjKeKgzo8Wm7gCAY5aWiIIK0o4g2WgEH9V1UZamOKACern0Jq89Ck8dB/jt5WMMj8EmVQlX7XzrcDui5Ss4d8p3IHgtgidCEg6Opa+onVVW58KUruPg/V1PbVM/dH/2MRkMtR+0ts33qwww4AbW26waz7RuLiXWqcFYKQghW1akMUEoZn9D2alB11VP7xR3E7vySxGPHaLRpD+AL/9D6iv68m+YjhMqqNze2aexsyNcM1Prwjlczil4halHWiVQTusg0TGOvxZO3g7r/PY30efHk7aH6uUfIv3QRB4ePxJVbjOJPdaK6W3c22PGHvzHwyE5MQlKYMRzXP56l5vd/Y8y00+NDXn91GW6dge8N1+wcOXWagbHB5WH0X77kxU8P43R4scZoMSiRyQO46977sEgf67fv4pO33+zwHjtLbxuR28JsNnPjjTeSmJjI0qVLOx0E2BkqinKx6SJIium+4RlAr4BA4nQGycao6CBI70usv+BQo6drgsJsPo9o68OsvugrNt66hSGuLJbp1/C+8zO+cKzmsOsoye54wmr6dk3/3REU/oeRo7Frb+je3MMU6o+Qbz7IOe+fTa5pHxZvBFWy9Xz8HpMOxd61a0kpkRtKKLTqmD01Hbe7liPeZMYb2/fHr/v0BmI2vKP9feGvGZ+lldcs/OyzVtvHJWUxfHYDtUUp7N+8rtU29Xs134P46YHVvog4O530v8wi9ZGZKKYSjAPnEnHBbcR8/16GbljF0O2byHjhTRRrIiApvPEW6j78EoCSIzso8K9Gm6mtqCFt32Zyxp3NjO0bWbTyA8YvnsPMGy4+fZ6lFbxV5GOcp4pFF80D4Ji/VGejy4vP7iUyJZyD95/Hzy7WdN1b164nOi6eH//il4Qrku2Hj7Lk5RcCus9A6WvV06mYzWauvPJKvF4vTz31FF988QWeLj7g2mLT/lzCfY1kDA6eeiu57Gv26UdhDus4yDQghBI01VOkwW+P9HbOLuf2+Ph8xzF26CXDLYOIi9UWNy9f8wbXWi7ljWmvsPHWzaw5/yteyX0YS2NIUPQK5m7uKNbnaA/dIb4xpLoHcHXkzcy2LKReX4Pb18qYEUbMPrVLK8rDe8pJrfNSNSUek05HUe1+3MJEfCtCSUpJzRc/wPaPNGL3ag/7uonnEzPlftIW3QJAbVnbwUBnX3wJRmstmz4o4MtVP2Tr1mfxnrKNVp0qqt6BKaJzcQeKopD484uQXidSTSD5Nz9EMZtQDHqssyYx4KP3iDj/Bny1xVQ8pKX4iji8G8eC81j+yps4HQ5eu+hGys6ZicnjIurqK9u93l///RFV5kh+c8UkwqMiibQ1UurxIqXkF19ou6K75g7CpFeYMG4EjZYYnJs+Zc3j91K/8wvu/tVvidIL9hcd581nngqap1J/EhQA8fHx3H777YwbN44NGzbw6quv0tAQvHQzn3yxBq8UXHFuYAuLMynJP8jOp2/i2IFN1NdUUnFkK5neQqozg2dDkooOoQZnR6FXFCw4aPQFNp6qqvz6iwOMWL2HW+urMUr45biTdWWio2L43dV/ZOwIzfyrj9FUvt4gBvB2he+MoOjujmJfzV5MXgtLbn6DFbd/zO8v+wWZEelIof7/9s47PKoqb8DvmT6T3hMCJKHX0AJIEQRBaYKoK9gWy7q21bWs5XPVtay7uuq6zXXFBqwFK6isClIVadI7BEggvfcy7Z7vjzskhJlJJiE0ue/z5CHce8+95yST+zu/TkZBltf1hmgrJiGoakMpj8LVWZSYBWPGJAOQHHMRBumk0uBdAbZ8x6tErvuEoMpqnEYd5RPuJnz6Qlx1dpa/uwerroJeV4z1+yyj2UqfsRJ7RUfKD7uorHqZb5de1tD/QLj1SNE2gWcItSFdpSC9y16bEmPo+OoTRN7+KNbJNwCQHaNGySS9+DwZgwYzLH0LAO/0ncotPzk5UOpb4O3+cRsfueK40lTKiJH9EEIweflnJC5+h4G/+5D1m3OJSwrlztRO5BzZR9ZfLuLWGLW21OZNh1j133ewWK3c++jjRJmNHCosZt4/Xm0XYaEoyjklKECtQTVjxgxmzZpFYWEhc+fOJSvL+zPcWnYdyUZffgxDXDcSY8JbPT7/6AEM86cwqOgL4j6aQtg/uhH7gZrDGzOs7X0jTkbqdO2WDAgQRB1VASgUxwqreGrlAeYbHYxw6nkjPJotl6TSIzHM7xidxYDOZsBdpgmKM4LV48Cub6NG4RQODIq5SfhoclQyAOl5R7yf5ym3UHlCuYtAyMksp0tePYf6hxNhVYWbQWcgVldBnrOpE95ZW4Bx1Su4DDrk4zkY/6+E8NFqRc2Nr39CaX0ME6bqsCU0H30SkaDuXlxyOHV1HbFaj1FcrIaZ6mON6J1BVGUcatU6ABzH8hGmOAzR/vs8xz10M8mvPkH6gMFYXU5ifvqJkr//i0PDRnCg30CmXvMyn3e/BICDZb5t1C9/9hMWxcXv75nacKxz3iGM0kX3mkNEdQzmi18Oo66mGtd/ryHGnU922r1ccd/dpES5OW41MBiN3PPIY8QHWTlWXsX81187JR+D2+2mrKyMiIhT63lwuujduze/+tWvMBgMLFiwgPx838EEgfLJ16uQCOZc2frdf2VpIa75V2LGzoY+v8ckGt+8B4096derdU2BmkXo2s1HARAkHFS5m3+V7swq45Idh3hLb2dijWDB5f2YMagjNlPLPdD1ERZcp+DvbA8uGEGhN+owWfRt1ihMioUaUznl1Y1qes8OaqTQ4cIMr+tDUtRdQk1262rApK/IoFYPaeObRoxE6uoodTf2t7AX7UD3cg+CK6qp7DcWYQpuiAipPJLOjoNx9Ek8QuepLVe13LfpGBLJ+OlXk5L8WwCKilTBEDl4MG5jNWXvZFC0dW2r1mLPKEDo9Ji7tRzW2OHWW4gqK+X7f/yX0ZdfyhUL3uHKTz9k9SPjmT1RTYoLMXvbaZctWs1qUyKXWyqJiWs0jyVaVHt2L30ZW34zlvggMzvee5QkmU32pa8zctbD9Bg1BWEwNgmk0en1XDP7Oowl+RwtKjmlrObS0lLcbjdx7ZRPcDqIi4vjtttuQ6/Xs3Zt636/J1JT78BZfBR3aAdSOrS+wMK+9x8m3p1P1uXvctG1j7DVpEaruaWAobe3r1bWjqYnAJujjuJavVqtwAd5JTXcuTsTmxsWdUxk3uT+rSpoaIi04NZMT2cOS4iJuja0Kf3pwHbWu1bS2zWYiJBGNbFLx07oFSOZpd7lIEKSQpFS4mxFNnhlSS3Jh6rZ2T2YpMimBeCCdS5qpEcryt+Ee/4k9AqUjvwFETMWNbl2z6I1gCBtzkQCofBILcJWR2xCFOHh6ku5vOKguo4O3Yi4MwW9y4r9Y0n2isVeJhlHdSXFO9ZTdmg7iqe3c/mSDZR/qfpU9GEtR0yljhqJWxgIXb6F7bnlDcc7h1qxexL4wizq+u0uhas/3sKcxdt5aY36s3/olqZlxiLC1F18WPEh8guKObpvM2m5H7Ax4gr6ekpCSykpqXAQdlIm+YH1P2ApymXIwIFs2rSJb7/9tk3C4ngxwOPlxs9VQkJC6N+/PwcOHGhzEcEvvt+GGRfDBvuvK+aPI7s3klb8BZvjrqHfCFUb6XHPRxT/ege6p8vocVn7ZGQfR+r07Wp6GuTI4KCxM2v3e/sQ07PLmbj5INlG+FtCHCO6x7S6HIs+woyrrB7ZjsKttVwweRQAoVEWyvJb5zMorSrjzh9vx6zYeGnan5qcMxgMRLpjyKn1LiOtM+mx6wSyInCVcdvyIyRL6DYuyetckM5NrcuEq64I5k/BbHdRMfOPRA64t8l17toq9h2OIik6h5DOJ9do9KaitBp3tYmo3urLPyGhHzt3hlFX9zWK8ig6nY7QxF7o7jVR+MEWTN/Fc2Tffwkf0wN7fgn2/ZUY8uLRSSNQRXnIF4RdlUTNysMIU2cUewWWvj4LAzfh8NzP0EsXhTGDWPevnST83zDiQlTfxvHdZEG1ndIgMxPf2UBJVhUgwRLNMylOOnZtal7TBwdDAeiQ7N62E9vhD0lAR4/rX2q4Jm/TMirqDQzr26fJ2Mztm0ns2ZtpM2ZgMJvZuHEjbrebKVOmtKq4XkFBAUIIoqPPbr/yQOjfvz8//fQTP/zwA5deemnLA05i1+5dCPRMHuWVm9siRSv/RTwmes9u/PsKDosiOOw0NV0S7SsormI33yiDeeYoLO0di97zGflkXQbPVZbj0sGSbkmkJretEKUhwgJuiVLlQB/WuvI47cUFpVHEdw2jJLu6VcUBl25dg0NfzwN9HiYpPtHrfIIxkXyZ5XPH6bIY0Af4LEetk/hd5WzrZGFgkvcHyqqDKmllwbfPU+2yUXfli4SdJCQAjiz5hjoljH7jA6thtO2HdASCXmnqi1an0xMRcRVWay47d33VcF1wYheSH7wKRpRjyu1E/Qcu5MowdGXBuPsWYLzGjZhQiXAZqJ1fizB1RpgL6fzqNMydmze9SEVSvegT6oPjiL3/CiLKXPznX9saNIkreqk9kZccKOByj5CwRJoBQUJ9OdfNmex9U32j7ffo4SN0K1rOvuDhRMQ0Zsrv/t+HGIWbXlfe0WRoeX4e0Z2SEEIwadIkRo0axebNm1m8eDFud2BhkA6Hg61bt9KpU6eGLnTnMp07d2bgwIGsXbuWJUuWtDrPwl1VghIch9nUurXW19XQu3Q5e8LHEhbZfh0hm6WdNYpgvYXrXZ+y2wrvrc/ErSj8e+1h7rVXEIbgg+6d2ywkQHVoA6eteVhAczhrTz4LdOgWjpSQdyTwzls1nnLE3WJ8Z5n2COtFmamIwiIff1ihJqyKxBVAjPWWNZkEuSThY7yFEUCk0UQtNh4Pv5l3+9xCcOodXtdIRWHbejvhpiI6jw0sPDFzVyGKzknfoY2N4QcPvhe328ixo+82uVan19NxxhXEPtwH40w3wb8Oo/OT00i58Tri0i4hccJU4u9tLCKs7xdY3Pux77YQXJSOdepMJozsTOjkjkQfreMf76q1icZ2CkcXZGDJqkyKsqoYN7ITt8WpNtubupkxWXzsshQ3SEm1PojCfZuJpRTZr7FUiaO2mv3phfToZMYU06jBKYqb+ppqbGHhgKrNTJgwgfHjx7Nz504+/fTTgMqG7969m+rqasaPHx/Qz+BcYNKkSXTv3p3Nmzfz3nvvBWyGUhQFk2LHGtT6PIc9qz4mlFosQ85YNZ92FxR6fRAjxWoG1cMT9RWMWrqDZ51VDK+FleP7Mzil7ZpR/eFyyhYfQh9mxhhz9lKzLyhBEZcSitAJ8tLLAx6TEqs2b8/IP+bz/IAO/UFINh3a5nXOGGPDKASVWc07tBWXG9umQvZEGbi4n+/aUM8MmMJ9ZrWJ4Pi+U31eU7JrJ0V1ifQfpCACsIMqikJ1Hlii3ZhO2AmaTGGYjJdgte0hJ8e7naYlIp644ZcQ3iXVyxRjjojjiYSj1G38Nxu2/tjiHAAqVqwGoPOv1R7Gc6b3oCI1DOvWMg5mVaDT6ejfXf1ji0gI4q2pfen36ds8u+1dbrttms97SreCAByhcZiKsnll38XkrviaBbdN4ssHZ5KzbglORUfKwKblyuy16sbAbGv0EQkhGDNmTEPZ7oULF7aYqLZ161ZiYmJISvI2I56rWCwWrr/+embOnElpaSmBNgqrqK7DIBSCg1svKJSD31JGCH1G+v5MnxZ0BkQ7mvsN+mAw1PHvIV2YYNcTpQhetoTx2aRUjAFENfnDnllBybw96MPMxNw9oE29XtqLC0pQmCwGYjoFk9uKqq5JcapJZl+eVxV1AIZ1V9tr7Mzb7XXO2kn9w6lsQYPZtTGHqDoF54h49H6iO6wGA0ZPVMXADr61m/3LtqLDRdcAk50O7c5CuI107O0dvtmv/30IobB33+sB3es4u0rS+Ta1H18lh5Hy1SKyjmS2OMaRlY3TGIQtsdFEdc3VPXDr4IslavTVXyf1YfCgeBbfPIytK9eQnL6fyBmXY7Z652iAql0h4ZpfNzpC92U5KKo2kJ7jZMtitdWrclINq9rycgCsod5NaEaMGMEVV1zBoUOHeO+997D7abVaWlpKdnY2AwcOPOdyKALhuKlMrw/sJWd3ujzXt97lmVixjQzbAPQBdGxsN/RGtcpMOyVV6g3q33mixc27U1L539SB3DgiBUMbWrUeR6l1UvzuHvShJmJu74/hLPkmjnNBCQqAiPggqloRavbaarWkQ4Wr3Of5DhFxhDgjOFDh3bQnzBMiW9tMiKyUEvvaXI4G6xg3vHOzc6l2qvOWJu+WmPVFhew7EkOX+FyCOgW2i929PhOAgaO9C8RFR/XB4eiJ270KtzvwSLGNJWriVt/7rgcEW/7xrxbHKIW5uMOa2qd7xIVQ1TsE4+4Kckpq6Rpu4/NZQ0gKs1H8+uuUhEcw7mb/5grpdiN1gkijm5tee4+r7/kVN//h96T1Uv+ojxYp9EoOpde1DzUZV16g9gUPj/Ot2Q0ZMoSrrrqKY8eOsWDBAurqvEtE//TTTwD06xd4r/JzieP+tkAd922VhcW5mXSQBdgT/VcMPh0InSoIpa+KCm3AYFQ/U87a9slwl4qk/KsjSLubsMkp6ENMLQ86zVxwgkJnECgBptsv3bya5Y4vGeAawfO/eNzvdZ1EF466vXsp2BKDUSS4iv3Xm8/YU0SHMid5g6MJMja/g9tm1zO4LhOD6aRwU0Xh4II3ccggBs8Y2Ow9TqTgcA1Y6kno5DsqJzp6EkZjLYcPr/Z7D5fi5o39yyitL6e4roQnc1UT0bA+g8i8eCwdV6+kqqb5EGF9eSFEe7+Yp0zrhsENHy9p1OZ2rPmR5L27KZ59A0E2/zbb5MhYbHYnn/7xCXZ9+SGJwycR1WcEY556n4tH9WDSjLFMfn4B4qRdc3m+R1DE+y8Pn5qayrXXXkt+fj7z5s2jurpxI1BZWcmmTZtITU0lLMx/xu25zHFB8c0337BixQpWrVrFp59+yjfffMMPP/xAZmZmk+uXf6u2n2mtNSf7wGYAIroG1l+l3dCrL17pbp8igwajWvzQZT91QVG9Ppecx9dSu62Q4IsTsfQ5TZFfreTCExR6XUCC4oedm/jdnnsJdUby2qxXMBv9q349QntSaiygrLKpiUnodTj0Air9RyvkrDpKiUkwZmxyi3MSUsHka/f23ZMYCtRyF8bEHi3eB6CyrBp3pYmIzv53K716zkRRBEeO+C+U99KeZfwhL5Znd6/iv4cbfRIhRhuJV15JUH0d67/6xu94t9OFubYEXZx3N7TBKRHkdneyvmAuWWUlAOT8+3XKQsMYd9ucZtcXYbEy5kg+gydPZ/vS//HVX/+s1l3S6xl231/pe/3D6E4yd0gpKcvLwWwLwhrSfP/j3r17c91111FSUtKkXtL27dtxu92MHeu/bMq5jsmkfiYyMjJYu3Yta9asITs7my1btrBixQrmzZvHqlWr1J9XWRnpB9UKyq01s1VlqwUnE7sPbNf5t4hHo1DaqRe1waQKCmd929qhnojTU/InfEZXwqd2abfeOafKBZVHAaDTt6xRlFSVcfe22wB4oN/DhAU3Xy55QIdUFh/6gA0HtjJ56Lgm51xWA0Y/SX6ZR8tIyaln05AIBgQ1b4OUUpJhiGCsPKmC7PrXYP2/OGRWX+aBhP4qisInf1sLGBkwxn/PgNDQTkg5HoNxJQUF+4iL864I+nmJ+kFeWJWCqFTors9h5ehJGHR6Bl06lo1h4dQt+QpmX+3zGfV5xeilCxnrO4T2UOTH7NNt4qmtb/NowgxSdmxh/y9vZWRIy45TvZSMu/nXhMUlsGreGxxY9z29Rvl+gTvqalnwyL1UFBYQk5QS0EuvW7du3HTTTbz//vu88847zJgxg7Vr19K9e3eios6NnWBb6NatG3feeSdRUVENrVyNRiNutxu73c6yZcsahEdOjppDZJd6QqNal1goig9QTgjhkW1v7tUWhEejaD9BoX4WXY5T1yiE1YAw6gge0T5tZNuLFjUKIYRFCLFJCLFDCLFHCPGM5/hAIcQGIcR2IcRmIcQwP+PDhRCfCiH2CyH2CSFGnHT+d0IIKYQ4I1lJer1AcTfvxLrjEzU/YYbleq65yHdUzYmM6Kmqztuydnid04WbsSKx13kLi/1Lj1Cnh5ETfDcNOpHNBzZQaIxgmO0EU8nuz2Hp49B7OnmVsXRLiyU2qfmdMMD/5m+gvsBE/AAdfdOaf3Zq6v1ICZt+ugOXq9Gmu7eqlvhV28mS8fTQ5TDYcJQZwcf430VjMHrMOXqDgYJLJ9Jl62YK8nzXEarNVrNZjTHev/6dR/azX6j2/s15X7J1/ru4dHpG33JTi2tsaIoBDLp8KlEdO7Ppy8/8Zljv/WE1FYXqXAzGwG3CSUlJzJkzB7vdzvz58xsS885ndDod8fHxGI1G9Hp9E+e2zWZjxowZpKWlcfjwYWJiYrjyhtv40D6YyJj4gJ9RXJBDbOUeiixJbXdytBGh8wgKdzsJCrPH9OQ4dY1CGHRIZ9uKcJ5OAjE92YHxUsoBqD2vJwkhLgL+AjwjpRwIPOX5vy/+DnwrpewFDAAa4i2FEJ2AiYDv2NPTQCCmp0SrGunkUgJLlkuMjifEGcG+cu9QUmOsDYMQVGQ03W3sOlBEnyO1pPcJJz6i5RIXv82sJNZewuR+o9UDlbmw6E7oPAKuepOQCAtuZ8tRHFvW7OfoxjoMUbXM/PWYFq+Pj+uDQT8Wmy2HtWufbjgeoW8UfE93i+fri2fwn2FXEmpuqn31u/F6DIqbDe8s8Hn/+jyPoIjzTrZ6ec2r6BUjV/V4GNwV1B9YRsZFo4hJaPmFJJ1O8LzghE7H4CkzKMo8QvbeXU2vk5KtX3/Bynf+Q0SHjnQbehGDA6iPdSKJiYncdNNNhIeHc9VVV52zRQDbCyEE06ZN49FHH+XWW2/FFKT6Yiwt+NiO8/1HrxL9eh96yAxKE9uYZ1J0ED6/AyrzWj9W7wkzbSeNwmhWN2cuZ+vquvlCGDyv5AD9qGeKFgWFVDn+EzB6vqTn6/j2NQzIPXmsECIUGAO87bmXQ0pZfsIlrwKP0Ho/WJs5bnpqTmL//boXiHV08tm9zh/J+m5kur1DaIM8O/yqzEZBoSgKxV8dptIkGD2jZZ/CvsObOWKOY5YrnegIz0vyyGpw22HqX8FoISIhiLL85p1ze7ccYf3CLDDbufbBiwMOfxw37m3q65Oot39FfZ3aijTBFkY45Uy1HWV8on9nZPc+vUhPG07HTxb61CpqjqnHgjo3vvxLK8v506K/sU23jsts05nRU32ZVNrqSbjh+oDmrNTUoDuhK1/viy/BGhLKlq+/bDgmpWT5W6+xav6bdE0bxo1/fpUZv3uCXiNbFqAn06FDB+6//3769u3b6rHnK1arFSEEdZ4MemsLOQPVtbXkPd2VMfueBqAeE2mznwzsYS47bJyratHLnoDXhsLOhfDuZCjxDiRpFoMaUq04W98CwPft2l9QSFf7hO62FwE5s4UQeiHEdqAQ+E5KuRG4H3hJCJEFvAz8n4+hXYAi4F0hxDYhxFtCiCDPPacDOVJKb3tN02f/2mPa2hxoAlBz6PSqmqs0U2BLSold1GLW+Y7R90XPsN6Um4ooLC1pcjysWzgA9TmNH6Lv1x2je7GTolHxhAa3HB+9PE+Vwb8aekLtpmqPryJCDYWNiLdRUVSH288H7PDebFa+nQ56N1c9mEZEVOsicmLDZ2Mw1LFx6WUoLjVM1ypc1ATQK7j3449hcjhY98cXvM6V7VT/yKNSVRPY0YIcpn98JR9Wvk2KszePTb2f1PAEjC5JbqRgyLiLA5qvUlOD/oSkOaPJzICJkzm8ZSMlOVlsX/o/Pv/zH9i5/FsGXj6N6Q8+jslyFpsSn8fUHxcULWgUB9cvIYFiAPYZ+6J7PBd9oGa+RXfANw/Dp7fAun9CbB8Y/yTUV8DccXBoRcDzFQb19yxd7RT1ZFO1aLe7HQSFUX0/yQCsA2eSgASFlNLtMTF1BIYJIfoBdwEPSCk7AQ/g0RpOwgAMBl6XUg4CaoDHhBA24PeoJquWnj1XSpkmpUyLiTn1WjANgqIZ1W7V7h+pMJYwKGJwwPftGa3mIuzLbtq3wRRlwQEonhDZmhoH4cuzORZm4OJLW/ZNuN1uFlYb6eIoICb6BMdzbYm6MzKqu+aI+CCkIqko9FanHXYn37yumlym/qY/HZJaX83UdbScw4eG4got5eCKKUhFwSgUHAHYUrv36cX+6VfRa8VSVnzXNFu7IrsEt86I0bP7f/Lr56jRVfJy33/w5a8+JiIkjAPbVuA0CGIqZMCx/apG0TTfZMBlU9Hp9Mx78C5WvPM6xccy6TNmPGOuvxnRimJ/Gk2pc/gWFFJK9ny/CJ4OY/Pcu6nL3NxwLvTKl5tUA2iWskzYswhGPwCz3odZ78Fd62DM7+D2lRAcA1/eC/bAfASinTUKvckKig53O/g8jldUOC81iuN4zEargUnAHOBzz6lPAF/O7Gwg26OBAHyKKji6AinADiFEJqoA2iqECNwb1kb0HtWuOUFxIEfd5TpF4IlmNs8Lu97ZNJlPCEG9SY+xSnUEr/1sH5H1EsuMLhgMLf/4j+Qf5rA5gXvCnOhONBXVloItqsERGJmgvhR9VcfNzypFOM2kjAwmpZfvWlLNIRWFrYcK0JV0IEnpS47xKEdWXc3g9WuIWb09oHvsuOxq6kxmDr0+r8nxiI4R6BQXboeTrMI8tov1jDVP4vK0xuixTzbMRe+WXLwvcKenL0ERHBFJ92FqLEXfSyZwx38WMPmeBzFaAtccNbxpND01/Tx///6f6bvyZgDSct9nVNYbAOwa/RqJfVvRKnWPp4z+wBug9zTofUWjAzwyBa58XfXZrX8toNs1ahTt46PQ6XXoFDNudzsInuPhsGexpLgvWgyPFULEAE4pZbkQwgpMAF5E9UmMRRUc4wEvA72UMl8IkSWE6CmlPABcCuyVUu4CYk94RiaQJqUsPvUlNU+jRuFfYq/I+Q6DMDKow8CA71teo/ogwoK8Q2llpAVbXjXbd+bRe28lu3qHMLVPYM1s8soLgBC6hp1UfdJZ22BrBQiPUwWVL0FRUaKqxGFRLTvNfZGzfQVF7hCm9Y2m6/iXcCwbS6ZpJ923qYL0D/ffgzWlGyUF+XQfNoJfX3ml1z22uXVEDBnBuE3ryM8rIT5BDR8N7p6EWC+pSj/GB+lfI4XCjUNnN4yrr61imf4AQ9MhrFrBkZODKbFlYeeuqcEY773vGHPDLYTHJzBsRvM9uDUC57igONGZ7Xa5GHTIOyv/sL4LianjvI775cA3sPxpiOkFUd18X9NpGHQaDodXwiWPtXxPj6BoL2c2oAoK2kOj8JiezjFBEYhGkQCsEkLsBH5C9VEsAW4HXhFC7AD+BPwaQAjRQQjx9Qnj7wXe94wf6Ln2rHE8Pr45jeIYhxliGM2koYEnTWWXq36EFB9tR62dQ9EJQfQHhyi16BhzTR+va/zh9PhJDCeXGzBaVQcf4Kh3IRVJcISZch+mp+oK1RYbEu5d+iMQtq1biQEn/SbMRuh09J64in6hN9IpRq2YG5yfhXHdCuIP76Hqw7dYt3ev1z0ynU6+HjwWq9vBurkfNBy3dlF9LJX7M1hetJR4RxJpPVIbzn/97T+pssI0+gNQtXRpQHP2pVEAhMbEMnr2LzFZ2yY0Nbyp9yEonLUVhFLDDwk3w9MVrO3xKNvSXqDrk9uIjG2FVrtlPgTHwS3fNB9G22EQ5G6H4pZb9gqP9i+d7SkoLLiVduhCd45qFIFEPe2UUg6SUqZKKftJKZ/1HF8rpRwipRwgpRwupdziOZ4rpZxywvjtHh9DqpTySillmY9nJJ8JbQLA6bGnGs2+HW/2Oid1hmpqlNbFRGdVH8XsthIb6u1HCe7TGC7pmJ5CSFDgcfphnh1GycmCzWBp2BEt+dcO3nzgewCqSrw/rFWeXtNhka0XFC57PbuLoU+4E0uomusg9Abi0p4hNrY7Rp2b6/49j3HP/53gGdcBsP6ZR1i5aVPDPaSUlBmgtktPCmI6YV32FW7PH0JIT9Xvsn3nevJNR5kQ07Tf8uKj/yOmWsek36gNh0reaVr63B9KVRW6AJLyNE6dUE/nwaKqxgoE5cXqxqnepGqOo69/nEHT7mrdjasLIX0ZDJgNthb6OYy4G0xB8G3LGoUweARFe2oU0oIiT/1+De1Uz5GM7ONccB48Z70bRKOgUNwK+9fnkbW/FJfTzY8b1SCsIfGtqz+T48wmRknwmdF7rNLOUZtgQbKRYYNbl3G53/MH1yXqpHEGC7js1Nc4yTuklg6pLrOT66OEeo2ny15EbMvJeCdz+Kdl2DHTv793gbug8Aicip4opY7B3bpyx/U3MOix5wDY9sqzDU1+9pbWoBh19Am2YLjiSpJLsvjxa7U/c2TfJJymYBYpy9ArRm4Y9YuG+2cc2MT2iAqmmoZgTuyIdfBg3MXFVK9Z0+ycpZS4q6rQt5BRr9E+DOgUDsC2Y+UNx+KT1TBhY7V398eA2b8EpBsGXNfyteGdYcgcOLwCaprfc54uQdEepidXQQ3oBIbws1st9mQuOEFhr3NhshgaXujrFh1mxfx9fPm37bxx7xo++F71z09MbV2tnmJdHrE6b5W6NLeGLe+nc+swG5uHta6sg1QUPi130dFRRLcOJ1V41RvB7cRe6+1wPzlHpK7aiUQhJKz1GkX6nm2YsNNluHe2cUyXngAU7VrXcGz8oEEUj1a1gmef+wNSUfjmmBoyPCYujLTbr8ehN5Lz/kJ1GSYjB4f2ZnO3ciYaJtHxhOzej1f9EyRcO+F+AOKf/gMABS+82OycZX09uFzoQjVBcSZIjrKRGG5l6Z7GPJlNH/8ZgOEli9t+4/TvIDxJ9U8EQo9JIBX44ZVmLzvuzMbdfh3jdFhROHXTkz2jElPHYESAyYtnigtOUDjrXTjqXCx7ew/fvLGLHcuz6D0ygUl39GP49C5sTFLbf0aFBJ5d61JcVOvLiTU1dVA76lx888YujGY9o+LCyHcE3oJ1Z8ZOBn23mg3WLtwVVO0dvqk3gdtBWLSVxB7hRCUGc/MLo5h27wAvrcZe4wK9q1X9no+TWVRNkqUWfbB3iY3Y/mpOQ+H+LU2OP3v3PVSERRG6bztlhQWsK64CKZmcFI0lIpzCIaPpsetHsnJVAbKmbx5B9fDo5b9puIfLYecb9w4GV0TQKUX1WVh69MDUtSuOjAzqdnn3/ziOu0o1G+rb0EhHo/UIIZg5KJEf0osoqKxn74ZvGbZfLdSwM/nmtt3UZVeTSrtfFniJj84XwdBfwYZ/q2P9oPP4KI77+NoDPRYUcWqCQnG4cWRXYU4596oOX3CCoudFCST3jyL3YBlHtqkJfGOu60HXQbGkTUnmXxf9B4D7Vt2HM8A+DAVVhUghibWqgVzVZfUs/OMm3nzgeyqK6ph0e1+6hlo5Vm/HFaCT6okDR3EIPS/bcrl5xBXeF+g9CfKKm469IynJqQYBSX29tRZnvYIwtj4uu6rgGMWuIJITfedd2Dr3JdjopCgzo8lxo15H7JgJuIJCKayuZq/Tga1OIdaq+mZ63HYTNpeddW8uRFEUNgcVMLI2kejYRvPa8u/mUhIsuarrzCb3jrjhBgAqvv6f33lLTwtPoSXQnTGuHtIRRcL7G45SsX5+w/H4i29p2w3Tv1Mj+7pPbN24ic+p0VGL74a6ct/XGNpfUOiEFUWcmunJnlEBbonZk6R7LnHBCYqOPSOYes8A5rwwimseS2PWE8MwnKDmje05ir+M+QvpZenM3zu/mTs1cqxItcPGh6gahZRQ4mlWNOrqbnToHkGK1YxLQo69+WYpDpeTB1d8wSZzJx4y5XHj8Cm+O4cdr1ejOOkyQHWgH9xU4POernqJvg0mz6NbvgMgud9w3xcIQUyEicIi76qZJfsPUNe5BzvySyk3C3qfkIHbecxFFEcnYvtuCQd2fk+1RTIovmly46IDnxFaJ5g08c4mx8OuUIs01m36ye+8Zb26s9OZz37DlwuFlOggJvWN590fM4moPsQBfTcOzFhCUtcAzUYn4qhRQ2Iju0KXVoTSAphscNVcqMqHrx/2eUmDRtGOpie9zoqiO7X72Q+WgUGHObn1vsTTzQUnKI4jhCAuOZTojt7mickpkxnfaTxzd86lpK7Ex+imZJephck6hCbgtLtZ8HijzT51vBoum+TZTWfWNS8onl+zmA90SYy1Z3CDD79AA55SybgdRHYIIqFrGLtWZVNf460FKU6BweJffXe73Xz00Ud88skn5OY2luzKPHwAEw7i+/v31+j1eqrrm2pJR9MPIj29Hj49Wgg6wZyujVqJEALjjJl0K85k6af/BmDIoMaeyblH97IxvJhJsi8mS9MwVn1ICLrgYOxHjvidk+JpUSrM55ZD8OfO/RO7Y7fX0ct9kKLo4fQcdLGaGLrmL3B0Xcs3kBIW3QWvXQSlh2HKX8DQBmGfOATGPgq7Pobdn3mdFkaPr65dBYXtlAVFfXo55pTQc84/ARewoGiJ+4fcj91t529b/9bitfkVqqDoGNWBHz462HD8zn9d0uAvSLGqL62MOv8fpo83LuENXXeut+9j4eVXYrE2s7NoEBSqYLhoZldqKux88bdt3kl3Lj1mm/8PX3p6Ovv27WPPnj28/fbblJWVgZRkljlJCnahN/gvtVBQUk9SXNM/5g1rVjV8H1WQgdVRz/Skpj6OwXf8khqTlWXhe4mo1dGrZ2P1+Y+/exW3XjD7knt9PtPcsyeyrg5nvm8NSnq0NmHSBMWZpFd8KM/Gq9FscaEes99Xv4VVz6vF++ZPb2oO+vJeeLU/uD2+u7wdsOMDNdJp9gfQbQJt5uKHIDENljwAFdlNTh0vM+52tU8JDwCd3orUO1DcgfshT8RVYcdVWIul+7lZeVgTFH5ICUvhtn63sfjQYjbkbWj22vzqQnSKHspN7Fufh84guOOfYxvKhQDEm41YdIJMP4LiWM5+7q9JoG99Nk+Pntxy4xydxxzlScTr0C2cyXf0p7rUzqJXtlJbqR53OJwIxYA5yP/LvqJCDa/91a9+hRCClStXUnV0O8VKGMmdmk+OMhkFbqXpXLNz8zC4nUSNGkt8ZSlX7PiRl7cdbTouNIQjs64lK0ZQZlPQCfVn5Xa7+Mr+E6llIXTv7bvMQ/AllwBQvmiRz/PSof6MdRZNUJxppk1UfQrd09+C7C2w70vo7wl5zlgDLybBX7rAc7GwdQFUHAOdZxNT7vmMXPch9Jx8ahPRG1QTlKLARzep2ooHnefb8qodVFbuPLXneDDoVc3XVd824WNPV9PLLD00QXHecceAOwgxhbD40OJmryusLyDIEca2pVkg4cZnRzTxewDohKCzxexXUCw+uBNF6Lk/IYjQ4PA2zTc5NZorHxqEo97Nhi/UelXHy3dYgv1XazkeDaXT6Rg6dCi7du1i7xa1eF9yX5/9qBoIDzFSXN7UnFalgEtv5N6J4+hw6SQia6vYsHtjQ17FcaY+dE/D93N/UpP516yaR2Gwm5kd/ZvdwmaqDu7q733nUygeH4VmejrzBPe9HH6zGayR8Jan18SQW+C6hRDsCX2uLWlq9pGeQIuM79UNUIT/routIqorXP485G6FQ8thw+vw9SMITw8LxRLCtu1zqKzyjqCrq8vh8OFXyMx8Haezwuv8yeg9DnKXvW0VZOvTy9GFGDHEnZsVAzRB0QxmvZnRiaPZnL+52etKHMUEOcIoya5mws29CYn0XWQuxWYiw4+PYlWdgS6OQi7vF1gZ7YaIDUPTZ0V1CKbH0DjSNxfitLupLFN3OLYQ3y/NnJwcVqxYQVRUFLGxsaSmqqGo3+xSk5biew1tdhodu3WlvF5P1TG1aZOUkpQjRxi6aRO5mzZx++jhHIpJJO3oAZ546+MmY4MsISyb+QMGV0f+uftJVh7Zzid7FhJSB9Om/NbvM43RUQibDftB7/4fcILpSRMUZ4fo7mrJjR6TIDQREgerGkKaJwIqPrXp9YvvUov6bV2gFv6ztKMzt9/VENoR3r9Gzdre9Aa8qQqwzml/xWAIYdu2X1JV1dh0LD//CzZumkLm0dc5fORltmydhdPpVVCiAUVxUOBW86/KluzHmV8TcIc66VKQbgV7ehmW7hGt7jt+ptAERQsYdUbq3fW4Fbffa2qMFQQ5wkmbkkzPi/z3/022mDlWZ0fx8SHK19noLyswGQJsY+7pCYHB+2XYa0QCLrubI9uLGjSKEB9d9KSUfP755+j1em688UYMBgMJCQnMmjWLPhFORht2oW9hPvGpowAo2aH6JSqPZjBs0090OZJB7qLFCCF469e3UBEchznvAFsPNDVBJYSG886k/4Bi4YE1t7M2vICJrh5YbM0ny5l7dEfW1FC3axf1B5o2mDpuehImLerprBHbC67/CB7Yo9YlAxh+B8x8A674W9Nrd34Erw1XNYuLH2rfeZiD4Y41au+KsY/B6AcbSt9YOo5h8KD30OttbNt+E4WFS9m1+1727H2Q4OCejByxmkEDF1Bbe4QjGf/w+4jDR16hTskk3jEL5aCJgr9tJe/PmyhbfAjF7v+9UbUmm9xnN5DzxI8ota5z1uwEmqBokTEdx1Bhr+CO7+6guM67NMB3R78j35nLhPHDGD69+f4SyTYzdYokz+4dmVSkDyXW0IpCYMeLBPqIe03oGkZotIUDG/Ia6jyF+hAUGRkZlJSUMG7cuCbtO3v37s21SWVMCGq5w581WhWMteVqgcDSDz5sOOfcuJH1t9xC0Y8/MnLQEBQJHy380Gu3NSgxiYcGPouiU4XfL0bd0eJzQydeBkDmL64lY8aVlH/xReNJz/3P1d3ZBcWJvwNrhFq3KXEI/GYLPFUGDx1UQ2Dtlao24WnE1a4ERau9K8b9H1z6FIz6LVz6BzCYsVo7M3jQ++j1Nnbtvpvi4uV06fIggwd9gNXakcjIUSQk/IKcnA+pqvIudllcvIpjx94iMfFG+k76Ex0eGU74Vd0wJ4VSszGP4nl7UGqduMrqkc5GoVGfXkbFNxmYU0IJGhpP8JhErP29k1rPFQLcvl64XJZ0GU9e9CTPbXiOBXsW8GDagw3nvs/+nke+f4TU6FRu7Xdri/dKDVZ3Vjuqakm0NO52i8oLqDbYiGlNR1hXPQi96rQ7CaET9Bgez+avM3F7cgnCo73V+R9++IHg4GAGDBjgfX9HTeNOEFCkgkBQkruFpTveZMLQ+4iL6UvRHtXRb4tUP+Smzp0bChmE5+ZCbi6lW7dy2ZdfcvBIH3Q5ezmcW0S3k5L45gwez4G3FQrCofvFfspJn0DEdbMpX7wIpaYWV24upfPnEz7D0+va4xyVyrnV/EXjBKI9v+OQOLhpkerIDm19r5RWIwRMfLbJIZstieHD/kdZ2XqCg/titTadR5eU+ykuXsHmLVfTseMcOibehNWaiNtdy959jxAc3Jvu3R4HQB9qInhYAsHDEqjdXkjpxwfJfW4DSNDZDETN6Ys5KZTq9Xnogo1E3dSnsU/2Ocy5P8OzjBCCa3teS2p0KjuLGyMkNuRt4IFVD9Ajogf/nvDvhsZFzdEvxIpRCLZWNm3BuHSPGmN+UaeW+2c34HY0hsj6oOfweJCQv1vVPCJjmgqK7OxsMjIyGDlyJEajj4goZ12DoDiQ/j/GzUvl8nmpTF82hxeK1vGrJddRV12IyarGpBuD1YzwuGuvBcARHITu0Uew/vUVTHYHWf/7msqCLAAMPnp1b8sq52vzkzz0pZ7S+QtaXL7OZqPrV1/RfeUK9NHR2Pftx1WsanzC4Lm/27/ar3EOIQREJDcmkZ4FDIYQYmIu8xISAGZzDMOGfkls7BSOHXuLdevHsH7D5Wzb9kuczlJ69ngavQ/N3jYwlpg7UwkenUj4jK7orAaK39lN3f5S6veXYBsSd14ICdAERcD0je7L3pK9uBU3Wwq2cN/K+0gKS+KNCW8QYgqs+JxZpyPZavLKpfihrIp4exFDa1qupd+AlI1hhT4Ij7UR3yUMxQVSuDFbmgqVjRs3YjabGTLET5Xc9KVqXDvw302vUCkgQoGOOiuPdphApk7y5rJ7iO6jZm2XHFKFaE2WKgzE5RPpecstSE8ZDYPZRHSnrgC4fLzAF246hjMknJDp06lYvBhnXl7AP4qo224FKSme+6Z6wBPFJTVBodFOmM2x9O3zCiMuWk73br/HYkmgqnovsTGTCQ9P8z+ucyjhU7sQPKID0benogsyUjJvDygQNKj1LYnPFpqgCJB+0f2oc9Xx5eEvuWfFPcQHxTN34lzCLeGtuk8ni4ljJ0Q+HSvK5ktbPwZV7UcsvgPq/EdXNEEqQPM2+G5Djn8Qm15XX1/P3r17GTBgAGY/kUHVQrDLZGLyO/35wlVEqj6Ej27bxcdzNnPjxFcZjZUVFQcJSRmATiiU5alJTfZS1VdhDFU1mJKNqmkqfuRILh46EICv12xs8qyqeidf7cjjitQOJNyj9izIf/75gE1H4VddpT77oOpTaYh68qUpaWicAjZbMp0738qggfO4ZOwe+vf37uLnD0O4mdg7B2DsEIQpORRjfNsaiZ0NNEERIP2i1H4MT617ighzBG9OfJNoa+udT52tZo7VNwqKL7YtRwodd2UtBCS82s9n2QEvzCHgrAFHrd9LjBZV4xAGhZqaGhTPizc7Oxu3202vXv7r8DzUMZnrE+PJ1kOqNDK7e9PWod2sceQIN0dXfoQidQSHq85wW0e1ZImzSC24aN+1C4fFQlivXgzvnUS1KZKCA9t5f9nGhvl8tSOPOqeb2cM6YUxMJOb++6levoKCP78QUJihPiwMhMBVqD7zuDbiqxWqhkZ70ZZgCX2oidh7BxFze//TMKPTRyA9sy3A94DZc/2nUso/CCEGAv8BLIALuFtKucnH+HDgLaAfIIFbpZTrhRAvAVcADuAwcIuUsrwd1nRaSA5LJsQUQrAxmLcvf5u4oMB6Xp9MZ4uJCpebCqeLENx8XWeiozuPYbP+CUIHc8fCp7dCUAykjPF/ow6DVa3i2Dq/pQ6O9wd3KQ5eeuklDAYD8fHx2O12hBB06OC/idKR4AioL+ZiEcRrv1zv9UcRYo3CXpfJvK9eI1IXTOpsNe8hKD4Bp17X8LI25+ZRExfXMP6OX87irbffJX3dN/y9qIgHbpjGwp+O0TMuhIGeBjiRN8/BVVBA6bx56MPCiPnNPbSEMJlwezLMnXm56MPCfLZC1dA42wghQH9+ReQFolHYgfFSygGoPa8nCSEuAv4CPCOlHAg85fm/L/4OfCul7AUMAI5ntnwH9JNSpgIHgf9r6yLOBDqh482Jb/L+lPfpENy6LnUn0tnjK8iqd/Dp+kVsC+3D7zLnqT1/OwyEGz8HnREWzIB3p8KuT33fqMslavbrFv8Vbs1Wzz5ASCZOnEhaWho6nY7q6mouvfRSLBbfiYEAeoOZqV2m8u9fbvC5czJ7atroy4LRhdZijFbDGqXDgcGtoHjuLQ0GhKKQl5fHwYMH6dohhuce/x111lhKDm5l+Y6j7MyuYPawTg3PEUIQ+8jDhM2cSfG//kXlty33ydaFhKB4+lC4CoswxLVNkGtoaHjTokYhVd3/eF660fMlPV/HQ2nCgNyTxwohQoExwM2eezlQNQiklMtOuHQDcM3J4881+kb3PeV7dD6hiuxL9TEMqNvPtV26Ncabd7sUHs1Qyyxvew+OroWyTDUR6cQXttECA6+Hjf+B6iII9u7VbfHUd+rWJ4lRo1K9zvtDSkmlo5Jgo//GP31jB2Is347dqFBqDKe6tITty74m+4fVDACsnToBYKitwY7gjTfeAGD48OFceuml/GLGFJYsnMcrH6/AZEhg5qCm0SZCpyPhuWepP7CfguefJ2jUSPQh/oMGTEmdqduyFUdODu7ycvTh4QGvV0NDo3kC8lEIIfRCiO1AIfCdlHIjcD/wkhAiC3gZ3xpBF6AIeFcIsU0I8ZYQwpc94FbgmzbM/7yjm82CXsCq/DyyjFFMKfkBXVinpheZQ2DqK/BYFvS/FlY+B/OvgGMbIWdLY4GzQTeB4lLLKfsgNiWUETO7MuqalvMSTiSrKosqRxUpYf5r7qSN/B1bfrkTS1gYhmqFD596mE1ffILDoMdpNGDevouq1auxlJZRb7EwduxYBg8ezMaNG/nTn/7E5jWqlmAWLib3iyfc5h3qKwwGEp55FldJCQUvvNDsnEOnqn0qSv/7HjqrtaHek4aGxqkTkKCQUro9JqaOwDAhRD/gLuABKWUn4AHgbR9DDcBg4HUp5SCgBnjsxAuEEL9H9XG87+vZQohfCyE2CyE2F3kcpOczNr2OnjYLO2pV002wq8Z/Fy+DSS15MO1VyN8J71ym1ql553LI362WSYju6bfto16vY/DlSYTHtq7Q2CcHPwFg1bFVzV5XV1ONObcWcx1Ul5Zw3bMv8cu/zyX5xRdx7d9P9p13IWw2LlnyFePGjWPatGlMmzaNoUOHUuz5XQoks4d29vsMa/9+RN1+OxWffU7BSy+hOHzXygq/Wo18ql6xAn1YGO7y8latWUNDwz+tinryOJtXA5OAOcDnnlOfAL7KjGYD2R4NBOBTVMEBgBBiDjANuEH6CW+RUs6VUqZJKdNiYrzNK+cjqSE2dtepMf4m6VKrXPpDp4O0W9WSB1f8Haa8DCWHVaf38mcgrg/k72rX+U3rou7ON+Zv5P5V9/Pe3vcoq/cO29Xp9eikag4bduW1JHTvCUDolCnEP/0HAMKmX4HBZvMsRUdaWhpTp07lmmtUS2MnXTkXdYlsdj4x991L+KxZlL79DhlXXUXd9u3eczGbMcTG4szORthsKNVtq+KpoaHhTYuCQggR44lcQghhBSYA+1F9Esdbn40HvEp5SinzgSwhRE/PoUuBvZ57TQIeBaZLKf3HeP4MGRDauMPvbgow+iE4BobcDMNuh9/8BKmzYO1fYc8iqMqDeu92pG2lZ2RPNl6/kZv63MS+kn28+NOL3PTNTV61rt5NX8DhDjW4O4dx0VXXNjkXMXs2PXfuIP73v/f5jF69ejFk9KX8YuYVLYYZCr2ehGeeptPcN1Bqasm87nryn/sjrrKmwivo4otBSmo2rEdnOzfLNWtonI+IluLUhRCpwHxAjypYPpZSPiuEGI0a0WQA6lHDY7cIIToAb0kpp3jGD0QNjzUBR1DDYMuEEIdQQ26P9xrdIKVs2iD5JNLS0uTmzc2X/D4f2FpZw5Qtqlw9FHmI4AFt9OMfWQ0rnlWLrE16sSEjub3ZlLeJ36z8DUHGIOJt8URaI4mxxvBZ+mdM7zqdp0c8jfEMlV9wV1dT9NdXKVu4EF1ICDH33UvEddchdDrq9uwl8+qrAQiZNImOf3v1jMxJQ+NcRwixRUrpP4W8pfGB1k0/F/i5CIp6t0Ly9ztJcZWyfsK4ptFM5yh7ivc0tIU9XH6YoroipnedzrMjn0XfTCmR00X9gYMUvvgCNevWEzR6NIl/e5W6LVvIukPda3R+9x2CRoxo4S4aGhcGmqA4T1lfXk0Xq5k48/lXZkKRCjXOmoBrXJ0upJSUf/QR+X98HlxqcIAhLpaQyy7za/LS0LgQOVVBoZUZP0uMCPefo3CuoxO6sy4kQE3Mi5g9G6Qk/5lnETYbXb/9Fp3V2vJgDQ2NgNEEhcZ5T/isWbjKygi55BJNSGhonAY0QaFx3iN0OmLuvvtsT0ND42eLVj1WQ0NDQ6NZNEGhoaGhodEsmqDQ0NDQ0GgWTVBoaGhoaDSLJig0NDQ0NJpFExQaGhoaGs2iCQoNDQ0NjWbRBIWGhoaGRrOcV7WehBBFwNHT/JhooLjFq84PtLWce/xc1gE/n7X8XNYB/teSJKVsc0Of80pQnAmEEJtPpXjWuYS2lnOPn8s64Oezlp/LOuD0rUUzPWloaGhoNIsmKDQ0NDQ0mkUTFN7MPdsTaEe0tZx7/FzWAT+ftfxc1gGnaS2aj0JDQ0NDo1k0jUJDQ0NDo1k0QaGhoaGh0SwXpKAQQnwkhNju+coUQmz3HL/hhOPbhRCKEGKgj/G/EELs8Zw/q2F17bCWSCHEd0KIdM+/EWd6DZ55+FyH51yqEGK952e+Swhh8TF+gOeaXUKIr4QQoWd0AU3ncqprGSiE2OAZv1kIMeyMLqBxHqe6Dr/jzzSnuhbPdfcKIQ54rvvLGZu89zxO9ffytBAi54R7TGnxoVLKC/oLeAV4ysfx/sARP2N6Az2B1UDa2V7DKa7lL8Bjnu8fA148l9aB2oVxJzDA8/8oQO9jzE/AWM/3twLPne11nMJalgGTPd9PAVafj+vwN/5sf7XxdzIOWA6YPf+PPdvrOIW1PA38rjXPuaBboQohBHAtMN7H6euAD32Nk1Lu84w/fZNrJW1dCzADuMTz/XxU4fdoO08vYHys4zJgp5RyB4CUssTP0J7A957vvwOWAk+exqm2yCmsRQLHNaIwIPd0zrMlTmEd/safNU5hLXcBL0gp7Z7rCk/3XFviVH8vreGCND2dwMVAgZQy3ce5Wfh/uZ6LtHUtcVLKPADPv7GnaX6BcvI6egBSCLFUCLFVCPGIn3G7geme738BdDrN8wyEtq7lfuAlIUQW8DLwf6d/qs3S1nX4G382aetaegAXCyE2CiHWCCGGnpHZNs+p/F5+I4TYKYR4JxBz889WoxBCLAfifZz6vZTyC8/3PnfaQojhQK2UcvdpnGLA/FzW0sZ1GIDRwFCgFlghhNgipVxx0j1uBf4hhHgK+BJwtOvkT+I0r+Uu4AEp5WdCiGuBt4EJ7boAD6d5HcdpTqNtN07zWgxABHCR59qPhRBdpMeW096c5rW8DjyHqrk+h2q+urXZCZ1tG9tZtO0ZgAKgo49zrwKPB3CP1ZwDPopTWQtwAEjwfJ8AHDiX1gHMBuad8P8ngYdbuE8PYNO59jsJdC1ABY05TgKoPB/X4W/8+bgW4FvgkhP+fxiIOR/XctJ9koHdLT3vQjY9TQD2SymzTzwohNChmi4WnpVZtY1TWcuXwBzP93OAL5q59nTjax1LgVQhhE0IYQDGAntPHiiEiPX8qwOeAP5zBubbHG1eC6pPYqzn+/HA2TTZnMo6/I0/W5zKWhbj8QUIIXoAJs5uxdlT+VtJOOG/M1HNts1ztqX8WZTI84A7fRy/BNjg4/hbeLQHzw83G7CjSvWl5/FaooAVqC+jFUDkObiOG4E9ng/0X/ys47fAQc/XC3h25OfpWkYDW4AdwEZgyPm4jubGn29rQRUM73mu2QqMP4/X8l9gF2qE1Jd4LArNfWklPDQ0NDQ0muVCNj1paGhoaASAJig0NDQ0NJpFExQaGhoaGs2iCQoNDQ0NjWbRBIWGhoaGRrNogkJDQ0NDo1k0QaGhoaGh0Sz/D9La52C55eEvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1055d7f0-e0f6-4377-9f01-13b1adaf6af5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the pct R of the cut district is 0.2865044843653209\n"
     ]
    }
   ],
   "source": [
    "print(\"the pct R of the cut district is\",cutTrump / (cutTrump+cutBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 50\n",
      "working on tract 100\n",
      "working on tract 150\n",
      "working on tract 200\n",
      "working on tract 250\n",
      "working on tract 300\n",
      "working on tract 350\n",
      "working on tract 400\n",
      "working on tract 450\n",
      "working on tract 500\n",
      "working on tract 550\n",
      "working on tract 600\n",
      "working on tract 650\n",
      "working on tract 700\n",
      "working on tract 750\n",
      "working on tract 800\n",
      "working on tract 850\n",
      "working on tract 900\n",
      "working on tract 950\n",
      "working on tract 1000\n",
      "working on tract 1050\n",
      "working on tract 1100\n",
      "working on tract 1150\n",
      "working on tract 1200\n",
      "working on tract 1250\n",
      "working on tract 1300\n",
      "working on tract 1350\n",
      "working on tract 1400\n",
      "working on tract 1450\n",
      "working on tract 1500\n",
      "working on tract 1550\n",
      "working on tract 1600\n",
      "working on tract 1650\n",
      "working on tract 1700\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%50 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "if type(tractMAP) != type(tractGeom[1]):\n",
    "    for geom in tractMAP.geoms:\n",
    "        x,y = geom.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "else:\n",
    "    x,y = tractMAP.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.8339285714285715 560\n",
      "100 0.5274872620005363 3729\n",
      "200 0.8095238095238095 1008\n",
      "300 0.796448087431694 732\n",
      "400 0.7294117647058823 1360\n",
      "500 0.8340336134453782 476\n",
      "600 0.4325068870523416 363\n",
      "700 0.4166666666666667 1644\n",
      "800 0.8230366492146597 1910\n",
      "900 0.8416050686378036 1894\n",
      "1000 0.7330689444783405 3278\n",
      "1100 0.8642779587404995 921\n",
      "1200 0.6529801324503312 3020\n",
      "1300 0.7081058378832423 2381\n",
      "1400 0.7807848443843031 739\n",
      "1500 0.86 100\n",
      "1600 0.42574257425742573 101\n",
      "1700 0.2948061448427213 1367\n",
      "1800 0.8036363636363636 275\n",
      "1900 0.0 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 10.894879133875014 10.421460332231435\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x2, y2 = origMAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  6143830 2699135 0.6182777037206635\n",
      "compare to Census 6910840 Leip has Trump + Biden = 2996186.0 0.6182777037206635\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 6910840\n",
    "atlasTrump = 1852475.\n",
    "atlasBiden = 1143711.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 6910835 6910835.0\n",
      "state Trumpers before, after slice opn are 1852460 1852460.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b5334311-b597-4568-88c7-eb3c9d4bdf09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 72 grids for 8 districts\n",
      "starting grid no 0 at x = -89.96177755555556, y = 35.19484037499999 \n",
      "starting grid no 5 at x = -89.52664866666665, y = 35.618673125 \n",
      "starting grid no 10 at x = -89.09151977777778, y = 36.042505875 \n",
      "starting grid no 15 at x = -88.6563908888889, y = 36.466338625 \n",
      "starting grid no 20 at x = -87.78613311111111, y = 35.19484037499999 \n",
      "starting grid no 25 at x = -87.35100422222222, y = 35.618673125 \n",
      "starting grid no 30 at x = -86.91587533333333, y = 36.042505875 \n",
      "starting grid no 35 at x = -86.48074644444443, y = 36.46633862499999 \n",
      "starting grid no 40 at x = -85.61048866666667, y = 35.19484037499999 \n",
      "starting grid no 45 at x = -85.17535977777777, y = 35.618673125 \n",
      "starting grid no 50 at x = -84.74023088888887, y = 36.042505875 \n",
      "starting grid no 55 at x = -84.30510199999999, y = 36.466338625 \n",
      "starting grid no 60 at x = -83.43484422222221, y = 35.19484037499999 \n",
      "starting grid no 65 at x = -82.99971533333331, y = 35.618673125 \n",
      "starting grid no 70 at x = -82.56458644444444, y = 36.042505875 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 497223.85659962194 3405256.9100904358 67.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 8   #EXCLUDING MEMPHIS ****** (drop 1 district from TN congressional)\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 3  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.447062281985338e-06 1.708610750001721e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 1701 tracts\n",
      "I am working on tract number 20 of 1701 tracts\n",
      "I am working on tract number 40 of 1701 tracts\n",
      "I am working on tract number 60 of 1701 tracts\n",
      "I am working on tract number 80 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 98\n",
      "we have 2 non-opposing shorted wedges for tract no 99\n",
      "I am working on tract number 100 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 117\n",
      "we have 2 non-opposing shorted wedges for tract no 119\n",
      "I am working on tract number 120 of 1701 tracts\n",
      "I am working on tract number 140 of 1701 tracts\n",
      "I am working on tract number 160 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 166 4.0 1 90.0 1.6836 337013.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 166 5.0 1 90.0 1.6417 337013.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 166 6.0 1 90.0 1.6009 337013.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 166 7.0 1 90.0 1.561 337013.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 166 12.0 1 90.0 1.5759 337013.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 166 17.0 1 90.0 1.5686 337013.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 166 1 337013.2851567246 1.5881\n",
      "I am working on tract number 180 of 1701 tracts\n",
      "I am working on tract number 200 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 214 2.0 0 90.0 0.4292 221489.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 214 3.0 0 90.0 0.3848 221489.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 214 4.0 0 90.0 0.345 221489.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 214 5.0 0 90.0 0.3093 221489.6\n",
      "I am working on tract number 220 of 1701 tracts\n",
      "I am working on tract number 240 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 249\n",
      "we have 2 non-opposing shorted wedges for tract no 250\n",
      "I am working on tract number 260 of 1701 tracts\n",
      "I am working on tract number 280 of 1701 tracts\n",
      "I am working on tract number 300 of 1701 tracts\n",
      "I am working on tract number 320 of 1701 tracts\n",
      "I am working on tract number 340 of 1701 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 343 3 468570.45150526427 3.1371\n",
      "8 yoyos for tract,wedge,wedgePop,r= 343 3 75200.79166010156 1.5686\n",
      "9 yoyos for tract,wedge,wedgePop,r= 343 3 468541.5864284586 3.1352\n",
      "10 yoyos for tract,wedge,wedgePop,r= 343 3 187116.5168861134 2.3519\n",
      "10 yoyos for tract,wedge,wedgePop,r= 343 3 353116.9083256146 2.7435\n",
      "11 yoyos for tract,wedge,wedgePop,r= 343 3 463076.96725684707 2.9394\n",
      "11 yoyos for tract,wedge,wedgePop,r= 343 3 432759.07168048795 2.8415\n",
      "11 yoyos for tract,wedge,wedgePop,r= 343 3 400752.1946322105 2.7925\n",
      "we have 2 non-opposing shorted wedges for tract no 350\n",
      "I am working on tract number 360 of 1701 tracts\n",
      "I am working on tract number 380 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 382\n",
      "I am working on tract number 400 of 1701 tracts\n",
      "I am working on tract number 420 of 1701 tracts\n",
      "I am working on tract number 440 of 1701 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 459\n",
      "I am working on tract number 460 of 1701 tracts\n",
      "I am working on tract number 480 of 1701 tracts\n",
      "I am working on tract number 500 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 518\n",
      "I am working on tract number 520 of 1701 tracts\n",
      "I am working on tract number 540 of 1701 tracts\n",
      "I am working on tract number 560 of 1701 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 567\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 569 4.0 1 90.0 2.8446 256381.6\n",
      "I am working on tract number 580 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 594 3.0 3 90.0 1.1258 274360.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 594 7.0 0 90.0 1.1237 255434.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 595 3.0 1 90.0 1.3317 197195.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 595 4.0 1 90.0 1.3052 197195.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 595 5.0 1 90.0 1.2792 197195.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 595 6.0 1 90.0 1.2538 197195.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 595 7.0 1 90.0 1.2288 197195.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 595 8.0 1 90.0 1.2043 197195.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 595 9.0 1 90.0 1.1803 197195.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 595 10.0 1 90.0 1.1568 197195.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 595 11.0 1 90.0 1.1338 197195.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 595 12.0 1 90.0 1.1112 197195.9\n",
      "we have 2 non-opposing shorted wedges for tract no 599\n",
      "I am working on tract number 600 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 602\n",
      "we have 2 non-opposing shorted wedges for tract no 603\n",
      "I am working on tract number 620 of 1701 tracts\n",
      "I am working on tract number 640 of 1701 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 652 3 260469.80407192325 1.792\n",
      "I am working on tract number 660 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 672\n",
      "we have 2 non-opposing shorted wedges for tract no 675\n",
      "I am working on tract number 680 of 1701 tracts\n",
      "I am working on tract number 700 of 1701 tracts\n",
      "I am working on tract number 720 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 724\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 730 5.0 0 144.0 2.9218 476292.1\n",
      "I am working on tract number 740 of 1701 tracts\n",
      "I am working on tract number 760 of 1701 tracts\n",
      "I am working on tract number 780 of 1701 tracts\n",
      "I am working on tract number 800 of 1701 tracts\n",
      "I am working on tract number 820 of 1701 tracts\n",
      "I am working on tract number 840 of 1701 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 843 3 491039.70246359834 2.1719\n",
      "8 yoyos for tract,wedge,wedgePop,r= 843 3 18030.470659639395 1.0859\n",
      "9 yoyos for tract,wedge,wedgePop,r= 843 3 491814.28423923056 2.1908\n",
      "10 yoyos for tract,wedge,wedgePop,r= 843 3 149548.1518430462 1.6384\n",
      "10 yoyos for tract,wedge,wedgePop,r= 843 3 362873.96881204384 1.9146\n",
      "11 yoyos for tract,wedge,wedgePop,r= 843 3 484660.5950909745 2.0527\n",
      "11 yoyos for tract,wedge,wedgePop,r= 843 3 464743.8850601327 1.9836\n",
      "11 yoyos for tract,wedge,wedgePop,r= 843 3 415683.1034420956 1.9491\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 844 4.0 1 90.0 1.3207 206336.0\n",
      "we have 2 OPPOSING shorted wedges for tract no 855\n",
      "I am working on tract number 860 of 1701 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 874 2 245571.21779371848 1.4167\n",
      "I am working on tract number 880 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 888 3.0 0 90.0 1.1228 261950.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 888 4.0 0 90.0 1.0712 261950.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 888 5.0 0 90.0 1.022 261950.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 888 6.0 0 90.0 0.9751 261950.8\n",
      "we have 2 non-opposing shorted wedges for tract no 889\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 895 4.0 3 36.0 1.5873 401347.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 895 5.0 3 36.0 1.5317 401347.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 895 6.0 3 36.0 1.4781 401347.3\n",
      "we have 2 non-opposing shorted wedges for tract no 898\n",
      "I am working on tract number 900 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 900\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 902 3.0 3 90.0 1.3686 233853.8\n",
      "I am working on tract number 920 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 920\n",
      "we have 2 non-opposing shorted wedges for tract no 922\n",
      "we have 2 non-opposing shorted wedges for tract no 923\n",
      "I am working on tract number 940 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 953 3.0 0 90.0 0.997 273036.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 953 4.0 0 90.0 0.7555 273036.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 953 5.0 0 90.0 0.5726 273036.4\n",
      "I am working on tract number 960 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 960 11.0 1 59.3 3.0931 455645.7\n",
      "we have 2 non-opposing shorted wedges for tract no 962\n",
      "we have 2 non-opposing shorted wedges for tract no 964\n",
      "we have 2 non-opposing shorted wedges for tract no 965\n",
      "we have 2 non-opposing shorted wedges for tract no 966\n",
      "I am working on tract number 980 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 991 2.0 0 90.0 0.5513 256707.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 991 3.0 0 90.0 0.4395 256707.8\n",
      "I am working on tract number 1000 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1010 5.0 3 36.0 3.2842 417579.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1010 6.0 3 36.0 3.0747 417579.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1010 3 416588.06771597423 2.9528\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1010 3 69630.04177825648 1.4764\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1010 3 416423.28239189496 2.9444\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1010 3 139802.7049021282 2.2104\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1010 3 294895.64523428166 2.5774\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1010 3 409676.1190582746 2.7609\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1010 3 373385.779078858 2.6692\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1010 3 397177.5031080214 2.715\n",
      "I am working on tract number 1020 of 1701 tracts\n",
      "I am working on tract number 1040 of 1701 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 1040\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1048 15.0 3 47.9 4.5903 486420.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1049 0 246572.67642105423 1.1246\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1049 0 85962.93664074628 0.5623\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1049 0 247399.88116586738 1.2328\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1049 0 118985.02396551061 0.8976\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1049 0 230544.61252834962 1.0652\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1049 0 164723.06350393515 0.9814\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1049 0 201857.8157895166 1.0233\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1049 0 182939.4545868522 1.0023\n",
      "I am working on tract number 1060 of 1701 tracts\n",
      "I am working on tract number 1080 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1085\n",
      "I am working on tract number 1100 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1110 2.0 3 90.0 0.7424 209810.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1110 3.0 3 90.0 0.6941 209810.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1110 4.0 3 90.0 0.6489 209810.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1110 5.0 3 90.0 0.6067 209810.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1110 6.0 3 90.0 0.5672 209810.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1110 7.0 3 90.0 0.5303 209810.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 8.0 1 43.4 3.8957 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 9.0 1 43.4 3.8572 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 10.0 1 43.4 3.8191 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 11.0 1 43.4 3.7813 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 12.0 1 43.4 3.7439 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 13.0 1 43.4 3.7069 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 14.0 1 43.4 3.6703 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 15.0 1 43.4 3.634 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 16.0 1 43.4 3.598 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 17.0 1 43.4 3.5625 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 18.0 1 43.4 3.5272 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 19.0 1 43.4 3.4924 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 20.0 1 43.4 3.4578 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 21.0 1 43.4 3.4237 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 22.0 1 43.4 3.3898 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 23.0 1 43.4 3.3563 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 24.0 1 43.4 3.3231 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 25.0 1 43.4 3.2902 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 26.0 1 43.4 3.2577 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 27.0 1 43.4 3.2255 377764.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 28.0 1 43.4 3.1936 377764.3\n",
      "loop31.0, tr1119,wedgePops727672.72, 11114.7, 332111.7, 11242.3, 373204.0, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0201 \n",
      "   targetWP, latest drx4 are tWP,dr, 372800.8,0.1801, 372800.8,0.2395, 11262.4,0.0002, 372800.8,-0.0133\n",
      "loop32.0, tr1119,wedgePops772903.85, 11114.7, 377342.9, 11242.3, 373204.0, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0201 \n",
      "   targetWP, latest drx4 are tWP,dr, 372800.8,0.1801, 372800.8,0.0682, 11262.4,0.0002, 372800.8,-0.0133\n",
      "loop33.0, tr1119,wedgePops772547.74, 11114.7, 376986.7, 11242.3, 373204.0, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0301 \n",
      "   targetWP, latest drx4 are tWP,dr, 372800.8,0.1801, 372800.8,-0.0054, 11262.4,0.0002, 372800.8,-0.0133\n",
      "loop34.0, tr1119,wedgePops740595.06, 11114.7, 345034.1, 11242.3, 373204.0, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0301 \n",
      "   targetWP, latest drx4 are tWP,dr, 372800.8,0.1801, 372800.8,-0.0516, 11262.4,0.0002, 372800.8,-0.0133\n",
      "loop35.0, tr1119,wedgePops771129.12, 11114.7, 375568.1, 11242.3, 373204.0, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0401 \n",
      "   targetWP, latest drx4 are tWP,dr, 372800.8,0.1801, 372800.8,0.0359, 11262.4,0.0002, 372800.8,-0.0133\n",
      "I am working on tract number 1120 of 1701 tracts\n",
      "I am working on tract number 1140 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1142 3.0 0 90.0 0.9779 292144.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1142 4.0 0 90.0 0.7002 292144.4\n",
      "I am working on tract number 1160 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1167 15.0 1 90.0 5.8636 759531.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1167 1 759531.3788829753 6.7284\n",
      "I am working on tract number 1180 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1193 3.0 0 90.0 0.7908 240709.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1193 4.0 0 90.0 0.664 240709.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1193 5.0 0 90.0 0.5575 240709.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1193 6.0 0 90.0 0.4681 240709.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1195 2.0 0 90.0 0.5766 248267.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1197 2.0 0 90.0 0.912 345184.2\n",
      "I am working on tract number 1200 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1215\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1215 3.0 3 98.2 1.5648 343798.6\n",
      "I am working on tract number 1220 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1230 2.0 1 90.0 1.0713 221482.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1230 3.0 1 90.0 0.9605 221482.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1230 4.0 1 90.0 0.8612 221482.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1230 5.0 1 90.0 0.7721 221482.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1230 6.0 1 90.0 0.6923 221482.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1230 7.0 1 90.0 0.6207 221482.2\n",
      "we have 2 OPPOSING shorted wedges for tract no 1231\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1231 2.0 1 90.0 0.9276 320568.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1231 3.0 1 90.0 0.857 320568.0\n",
      "I am working on tract number 1240 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1244 4.0 1 43.8 1.49 453923.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1250 6.0 1 60.3 1.3164 447240.7\n",
      "I am working on tract number 1260 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1270\n",
      "we have 2 non-opposing shorted wedges for tract no 1275\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1279 1 284941.2398655279 1.841\n",
      "I am working on tract number 1280 of 1701 tracts\n",
      "I am working on tract number 1300 of 1701 tracts\n",
      "I am working on tract number 1320 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1320\n",
      "I am working on tract number 1340 of 1701 tracts\n",
      "I am working on tract number 1360 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1362\n",
      "we have 2 non-opposing shorted wedges for tract no 1366\n",
      "I am working on tract number 1380 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1387\n",
      "I am working on tract number 1400 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1417 2.0 2 90.0 0.9153 241105.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1417 3.0 2 90.0 0.7675 241105.0\n",
      "I am working on tract number 1420 of 1701 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1430 1 288300.36353009753 1.6893\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1430 1 40620.53285143434 0.8446\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1430 1 288300.5998953525 1.6907\n",
      "I am working on tract number 1440 of 1701 tracts\n",
      "I am working on tract number 1460 of 1701 tracts\n",
      "I am working on tract number 1480 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1496 2.0 2 90.0 0.845 264773.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1496 5.0 0 90.0 0.9199 213056.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1496 6.0 0 90.0 0.85 213056.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1496 7.0 0 90.0 0.7853 213056.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1496 8.0 0 90.0 0.7256 213056.7\n",
      "I am working on tract number 1500 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1503\n",
      "we have 2 non-opposing shorted wedges for tract no 1504\n",
      "I am working on tract number 1520 of 1701 tracts\n",
      "I am working on tract number 1540 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1552\n",
      "we have 2 non-opposing shorted wedges for tract no 1553\n",
      "I am working on tract number 1560 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1579 7.0 2 90.0 4.2713 202407.2\n",
      "I am working on tract number 1580 of 1701 tracts\n",
      "I am working on tract number 1600 of 1701 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 1602\n",
      "we have 2 non-opposing shorted wedges for tract no 1603\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1603 2.0 2 30.6 1.1104 331519.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1603 3.0 2 30.6 1.0395 331519.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1603 4.0 2 30.6 0.9731 331519.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1603 5.0 2 30.6 0.9109 331519.5\n",
      "we have 2 non-opposing shorted wedges for tract no 1604\n",
      "we have 2 OPPOSING shorted wedges for tract no 1606\n",
      "we have 2 OPPOSING shorted wedges for tract no 1609\n",
      "I am working on tract number 1620 of 1701 tracts\n",
      "I am working on tract number 1640 of 1701 tracts\n",
      "I am working on tract number 1660 of 1701 tracts\n",
      "I am working on tract number 1680 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1684\n",
      "we have 2 non-opposing shorted wedges for tract no 1696\n",
      "I am working on tract number 1700 of 1701 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0003183723507976\n",
      "Average and max number of wedgePop loops per tract were:  6.105232216343327 35.0\n",
      "min,average,max of Home District areas were:  0.0 1.1668084854648932 3.4522773544786185\n",
      "calculated statewide vote was 0.6620392312104321, should have been 0.6182777037206635\n",
      "calcd statewide Hispanic pop was 0.050209688482963206, should have been 0.048667529765679944\n",
      "calcd statewide Black pop was 0.08830217377165372, should have been 0.09218209470081845\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.5167548500881834\n"
     ]
    }
   ],
   "source": [
    "#3/7 - 3/10/22 - linear wideAngle, short the opposing wedgePop to match first shorted wedge (see nonper toy)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #NEW 3/7/22\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opposite wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                #now, we \"squish the square\" based on how close we are to boundary - see offline documentation\n",
    "                equivL = (4.*minWedgePop[t] / avgDistrictPop)**0.5  #non-dimensional centroid-boundary distance\n",
    "                wideAngle = avgWedgeAngle*max(  1,( 1.+(maxAngleRatio-1.)*(levelL - equivL)/(levelL - 0.) )  )\n",
    "                wideAngle = min(maxWideAngle, wideAngle)  #prevent too wide of an angle (half-pie)\n",
    "                #HERE, WE ADJUST all wedge ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit to avoid gaping wedges\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                # NEW 3/10/22 - we not only squish the angles, we short the target for the wedge opposite boundary\n",
    "                oppWedgeExpectedPop = minWedgePop[t] * math.tan(0.5*wideAngle)/math.tan(pi/nWedges)  #prediction for new wedge0's pop\n",
    "                oppWedgeExpectedPop = min(oppWedgeExpectedPop,avgDistrictPop/nWedges) #not needed, but just in case...\n",
    "                for nW in range(nWedges):\n",
    "                    if nW == int(nWedges/2):\n",
    "                        targetWedgePop[nW] = oppWedgeExpectedPop\n",
    "                    else:  #NOTE: we don't mess w wedge0's target.  When it shorts again, we will adjust other wedge's tgts\n",
    "                        targetWedgePop[nW] =(avgDistrictPop - oppWedgeExpectedPop)/float(nWedges-1)\n",
    "                # *** END OF 3/10/22 MODIFICATION\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0003183723507976\n",
      "Average and max number of wedgePop loops per tract were:  6.105232216343327 35.0\n",
      "min,average,max of Home District areas were:  0.0 1.1668084854648932 3.4522773544786185 for  TN-noMemphis\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start TN-nilMemphis HD  7:46 - 8:44\n",
    "STATE = \"TN-noMemphis\"\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN-noMemphis 23Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"23Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"maxAngleRatio\",\"levelL\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, maxAngleRatio, levelL]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD2tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26 0.7892685902681283 2712 pop,GOP 0.3573008849557522\n",
      "127 0.7872418887259246 242 pop,GOP 0.6694214876033058\n",
      "128 0.7752238213024308 1914 pop,GOP 0.48589341692789967\n",
      "163 0.7868663835882711 829 pop,GOP 0.6936067551266586\n",
      "936 0.7897164573760542 923 pop,GOP 0.7724810400866738\n",
      "937 0.747175734760196 133 pop,GOP 0.8571428571428571\n",
      "938 0.7684805446899552 962 pop,GOP 0.6912681912681913\n",
      "939 0.7693218478530357 512 pop,GOP 0.70703125\n",
      "940 0.6165068778170767 434 pop,GOP 0.8594470046082949\n",
      "993 0.7873943737113349 1225 pop,GOP 0.5273469387755102\n",
      "1081 0.7803819649559544 1024 pop,GOP 0.8623046875\n",
      "1082 0.799779779058346 1020 pop,GOP 0.9117647058823529\n",
      "1083 0.7755364209906704 510 pop,GOP 0.8588235294117647\n",
      "1152 0.5982487797889559 3424 pop,GOP 0.5703855140186916\n",
      "1161 0.7290256854229982 3411 pop,GOP 0.7423043095866315\n",
      "1164 0.7689863738842819 5116 pop,GOP 0.7075840500390931\n",
      "1165 0.7510394569233572 1810 pop,GOP 0.6685082872928176\n",
      "1166 0.7398513186665643 1905 pop,GOP 0.6603674540682415\n",
      "1174 0.6695477683283503 3857 pop,GOP 0.5029815919108115\n",
      "1175 0.677135997491164 3525 pop,GOP 0.625531914893617\n",
      "1194 0.7890724685099394 3493 pop,GOP 0.6687661036358431\n",
      "1211 0.7918359621693878 4014 pop,GOP 0.6285500747384155\n",
      "1215 0.7057572037170355 2947 pop,GOP 0.39667458432304037\n",
      "1216 0.7687310889168218 2445 pop,GOP 0.3169734151329243\n",
      "1229 0.7821208864171717 2201 pop,GOP 0.4993184915947297\n",
      "1230 0.52142596321321 4139 pop,GOP 0.33679632761536604\n",
      "1231 0.7492428028480923 4377 pop,GOP 0.2280100525474069\n",
      "1232 0.7277239905225007 3355 pop,GOP 0.3317436661698957\n",
      "1274 0.17358981605470608 2345 pop,GOP 0.4319829424307036\n",
      "1333 0.5592354943105295 1110 pop,GOP 0.43513513513513513\n",
      "1339 0.7486860019800134 1975 pop,GOP 0.7058227848101266\n",
      "1341 0.792697207651606 3819 pop,GOP 0.7336999214454045\n",
      "1342 0.7744239416214946 2960 pop,GOP 0.7415540540540541\n",
      "1348 0.6462517933305182 2250 pop,GOP 0.7435555555555555\n",
      "1349 0.6228913677643978 3627 pop,GOP 0.5767852219465123\n",
      "1354 0.774606162528045 1911 pop,GOP 0.7378335949764521\n",
      "1362 0.5932145679404714 4053 pop,GOP 0.4463360473723168\n",
      "1447 0.6135293261796492 2866 pop,GOP 0.7330774598743894\n",
      "1448 0.7535288074452197 2796 pop,GOP 0.7879113018597997\n",
      "1449 0.5791661370581519 2757 pop,GOP 0.8099383387740298\n",
      "1450 0.6383831926168826 3301 pop,GOP 0.8079369887912754\n",
      "1451 0.6704639783988952 2857 pop,GOP 0.8977948897444872\n",
      "1452 0.7101485271496151 4484 pop,GOP 0.7526761819803747\n",
      "1557 0.7459515806279015 1353 pop,GOP 0.8004434589800443\n",
      "1817 0.6022180381632086 10907 pop,GOP 0.7516273952507564\n",
      "1822 0.6427127401575853 10689 pop,GOP 0.7485265225933202\n",
      "1823 0.6330878842162373 11504 pop,GOP 0.7515646731571627\n",
      "1833 0.6687720647074746 11700 pop,GOP 0.7555555555555555\n",
      "1839 0.7005284246197503 12520 pop,GOP 0.7545527156549521\n",
      "1841 0.09317185240368546 881 pop,GOP 0.8365493757094211\n",
      "1842 0.6984956553357942 12205 pop,GOP 0.754854567800082\n",
      "1873 0.43938551545395543 1442 pop,GOP 0.8169209431345353\n",
      "1874 0.6445814104298576 11582 pop,GOP 0.7449490588844759\n",
      "1876 0.38756910786666965 3372 pop,GOP 0.8075326215895611\n",
      "1882 0.6418958623063108 504 pop,GOP 0.8353174603174603\n",
      "1945 0.6341199838626765 471 pop,GOP 0.821656050955414\n",
      "1947 0.7427159644550247 11449 pop,GOP 0.7539523102454363\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in TN-noMemphis\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 1 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1431940677283139 = TN-noMemphis std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.14660828736669115 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for TN-noMemphis\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  7.5199\n"
     ]
    },
    {
     "data": {
      "image/png": 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c7r8OeAfwNWZof6y2hknvi5k/iw9gOLXxQ/zfZZUOTndGly/Jj7F81ATLl6D621lZR5JPAm9j+dL754D3AX8PHAF+BHgOuKuqWp+AsMo63sbyxxcFnAZ+79XvDbpK8vPAvwBfAb47DL+X5e9vZmafXGIddzND+yTJT7N8EsQWlg8KjlTVHyb5IWZkf1xiDX/NBPfFXARKkjR/5uEjPknSHDJQkqSWDJQkqSUDJUlqyUBJkloyUJKklgyUJKml/wGQWSoO7/FcswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00966 0.62794 HD avg vs true 0.61828\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.03368, should have been 0.03649\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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trqrdwJ8Cv7xB4wRj7BfwUeDHk2xO8mLgR4CzKzxnB+Ps1RdYeKZJkpcD3wecW9Ep147jwM3Du/muA56pqouT3qnPoFZYVV1KcjvwIAvvJLqnqs4kuW24fjfwm8B3An8wPDO4VBvwtyuPuVcajLNfVXU2yV8Bp4GvAx+sqpFvHV7Pxvy39dvAHyV5nIWXsN5VVRvyf8OR5E+ANwBbk5wHfgt4EfzfXp1g4Z18c8CzLDz7nPxxh7cISpLUii/xSZJaMlCSpJYMlCSpJQMlSWrJQEmSWjJQkqSWDJQkqaX/BacyS9eVboe7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for CO\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 0.01\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for TN-noMemphis\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 0.199 TN-noMemphis seats out of 8\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the state map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  772147.75\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L59RyY8MsVqxtRXHu4bH1bXmF6nWL6nI87aI2N4h/XsqhFG1AJeOPKsWJprJL0ovsliL7bwNui9neApyTVLuON0EDe7Ggw7jUKvl0955NQoCbzpsVihUJ/rA8+4jiCIxt+w7HtnPJvImMqk6FXHmVsOrqxXc7QrYMBbqO9JYUuPjY+v7ZPzhGZ0+9k1Xoc+/HG8SWPbmRTMaxJUS0X/T0ZdkdtMOkU8yuHc3m3Yf8PvnRKy3+aui/felsP85mTctef9XxXGQV0Xm4JyeJ5VnTx7FpZxd3PdVMc/sBsgo1u7q4/dLTc/owbvDIN8g94gqN6qoUd36hno3tB3zBExyQ2/cfYeXrrfFec25/dx7u4VuXnwFQUvaEcvHsYF5fP9K4jb6sFrWzBAlOOI61jaVqAyoZf1QJTkSV3aAb+Ucy0Qcq6sobPSYVMZZ7P4zgbPYnjdv8wRicQWbcqNyv8fpFdSiwcMZ4X2hlFV59fw/rtuzLebi9H+MPn3+PV9/f4wcieqqqNS17c1Q0APe+0gKe+g1iE1w2be0MrbjAUTF5thrP28m7rYz2v8m3Qpnk2mE8wfzSpg4+3HMIVcdwH7zWnz+xgda9h7jv1Q/pyypVKeG2i08j5dpcPGrH1HD1wum88n5/+NVpk07mu49vCH123KCYz+Ei3yC38uv92zbt7OIn67aRyTqeZMFzV6xtJSWCupOEIEGngnwTmWLJRqH4rHrxnFqWXVPPMxt3cFJ1mhfe2VXUzuJtL0folko5qq9icWDHkxNRZWcCJkGiD1Rwphl3DG4chdAfrBedza4IGJo97nv1Q66snxY7k7x+UV2O4Cg0AHznio/5g37wx18o9gScwe6i+ZO4euH02FVY0F50xuST+cEN57B4Ti0Lpo1l1fo2Xv9wH5s7PiqpX6tSYaN1MEYlJc6gElRzZRSWuysacHKULX+5JXQ/AjS3O15daVfwVKWFD/ccyvn8uBVo0OHiaG/YzTlukAsKkD97YoPftu7eLD98/j3ffnTX081ksurY0Qi7Zl8csDPFTWSKJRtt2trJY+vbYlckQZq2dvarMtMp31mhkJ3FE3pxM/ZjXVlEY36OV+qjUsn3+UNRZZc0JmDKpJyHt5QHKnpMcHCIxqSsWt9GOi2hFQz059sCQuoqL15jxoTRodWCN7P97uMbfG8mL9Av7sfftLXTd8vNRzolfOeKj8XO0pw4lP5AlJbdh7h39Qd8w1UzPdq4zRcQ+Qg5KqT6yxitadkbOjer0HW0L+f86OIrqnZDHNdjzzsMIJNRRlWFSyadMflk/uDieTkeeNmIH7TXX1Gvsmg8iOMt1n+e0r/K9Nx5PcHSMLeW37Tu9+ONvnPFx/xA0riJTCFVVNR9HfLPqqN2pJtdZ4h88VTe9uDn9xRxUy+bQMxPkMFWQxX6/KGmsjsemIApg3If3lIeqHwDenRGGjSGf+6sqex34z5UoaY6N3IbnEHp4UbH4ytuZhu0t3iBfkAo6HLTzi7ufHJjaBCvSguXL5jC6vd205cJJ6TctLPLV+kgwtNvtvPerq6Qe20WePbtXfxyUweXL5gSuvYZU06hde+hkE0mOHOH/kwCa1r20nWkN6dPW2JWHcVQhb6IEFKgqbWTmbWjEVV2HjxKy55D3PV0M4Cv7lwybyKplJAN3EdfFh5c2xpSecWtLuPUjt4qUyDkwfVm2wFuu/g0mncc9J0YoHTvreAEJye+ifyu5HFeYPme5eD22jE1/veW1fJr6OTDWxF7dsWg4BpsNVSxzx9KKrvjwYgSMB1d3TRt7RxSbo+lPFDRY6KfEzSGZ7LKObMm+EZrL6r7pU0dIeHi4Q3UPb25M9som3d15UTti+TO/m9qmMVffvnsnNWcZ4fwVEWZrPLOzq68992XUd5uDwcbzpt0Mj+4/hMBj7Xc8xR46PVW34gfpTvm3uIQ95/gZwiO51jGvwfY3nkkdF5Pb9YPeBTg2nNn5A3kDM7co9+r4qz8gurDqpQjnL2BHPA9uPr6sk4ST1VeeX8PAr69p1htnkLqmnQ6FUqt41Es43Uxgp6JguPGPhCPsSiFBGd0Xyk2qEpSKTXYYKv5KsWIEjC7Dh7l1vvWJLYsPl461OjnBAMXAd7ctt+PWve8s2LGWdIBY3cW/Nm+l5gxqtbZvv9IjhdZdOAMeggFZ42bdnaFosxLJdruXQePsmlnV47HWpSQQ8AASAFn141nz0fdbN9/1N9++uSTueLMqdzzckve84JOBAo88UZ73s8RkbwriusX1TFuVBXLX2lB1REWcY4gq2IyJnif7XkWzoyorIoRNNx7sUtB8mW8LgfPM9ELuP3V5rCDyUAH0UKC09vnuaLf+eTGgvalUiinnZVQgw22mq+SjCgBA8kui4s9PMcy64ieG40buGT+ZJ5/ZxcZddRLL23q4IaGWf6MOCVhgQK5Hlj3vfqh/38mqzn5udoDA22UU8dUc+opo/iDi06L9YATBjbYb99/lGrXrqTAW20HaG7fGJrVF2Nm7eicVUYhUkAqBRvbD+aoqE4eVcXB7r5QrrMgZ9eNp6cvW3BlFuS0iWP813Hf6/2/3kJWnZXMsmvqcwb64DlBo31Wnfvw4mT6MrmDkRd7lC+Bp3et1z7YS+veQ4wdXe0/f5VQNQU9E3+1eU/e7NkDGUSLaQaiQbgDvYeBDPbHqgYbbDVfJRlxAiZp74x8D8+xRurnO9fL1BsNtOt1Y0SiDgLPbNzh/5ijA2RfVkPeVKKOUPIEVKExfd/hXvYd7mXZk/0VIoM/hLgVVCkocGPDLLbtO8wrrpdbxnUlDgoZCRwfpCotdBzMLxg9UgJnzxzPhfMmcrC7j4ddt+Aob7Ud4O0dB/PeT/OOg2TLEH4f7D7ETfe+5ud2Cz4/S3/c6KsqM1n1vdiCRCcewQwJze0H2Lj9ABu2H4gdjIKxR8EEntGia1lV7nm5JaRyq9RqPZ9nYpKDaDn2pVKvdTwH+5HkbTaiBMzUcScN2nLyWB7EuHMhN1NvkOq0UD9jvK/f9gyvC6aNZd2WfbHnCeEVSyolfObjU/zBNC6nV5Q+1xlg8ZzanBidvqwWFFKePj5IWpxYnT2BZJMKfOr0ibwciEeJu+zciWO46IxJrHw9v4fbpFNqmDfpZN5ocwbiTbu6nBihPPo3hVDm5CiZTG48ShQBpo4bxc6D3XlLNzdt7eT5d8KBnu/t6vL3eULkzqeac1YgnpAIJrl07GX9yTGbtnayNhI06tko8z1XnsrNy95QKY+nfCv/pAbRUuxLA7lWoXZW0mYykrzNRpSAmTJ21KB9Gccy64g7NzoL80gJXHHmVC5bMCUU4xC0izx42xLuXf1BTr6rVArSqRR9mazvNuxFs6fEUbek0FwX3gieQArquwU41N1X0B4RnFGOrkkzpibNvEknc+dTzSGnA6F/sC3EVW5hsVUFcpLt+aiHUVXOPQeN6zVVqbznFCKdFlIifh8umj2Bnr4sb7Ud8K+lwM6D4ezM0dLNjmtz+NrrW/ezYm2r/72K9DsbBEsEeDFR3vPhOWN4gqx17yEeeG1LTg2cjoNHuXf1BzlOHL77N+GZ/rGoeqIDbvRaSQ6ilbx2KdeKBkt7q9VjYaR4mxUVMCIyBvh/gNmq+nURmQ8sUNWnE2/dMKKchzrux1dohgf99g0Bzpk1gc7DPXlXTIvn1HLOrAk89/au8ACqcMPiOmZOGM0b2/aHUqVkFTQwc49bbXhtum5RXWiW7aliStUcKXC4J8Phngx7PsqtnaLArsgAHccDr23hyvppLLumnu9Fou2DeEZ8L9HnQjfnWPP2A7zZlquWinLqydV0Hup11ElZ5ZNzJvjZA5q2dlKVkth6M9AvjFOpcOr9JfMmUp2W0Gopm1WWv/xBKPA2yO6ubr8uTjrlXNP7zkJBpK7TQLQ5jhDsv18vTY3nuh5XUnsglJPKZTgMosXaGdRAZFX5859uZGP7AasiSmkrmP8FNAEXuu/bgEcAEzARSvnBFIpuzjfDi6bDjyZEDEZO37v6A3YdPMqFngdPwCDseS4tnlMbOyArjk0jm3EcACQlOSqhc+rGO7m5YmbZlaSUKx7tzXLXU81MGXdSSWqri84IZxpIpcR3jBDgvLm1fOmTdfzFkxv9gb8qLex3hQs4A/m6LZ2kpT8+p7eA2szb3pd1Bh4vJX/cPSpOlc1YwZ4WJ3jWbVdf1iluFnesatjVOs5+JTiTjVJm2uUUtYvaV3r6+jMTHK/BNikvrEIR+kGvzExWQwXvjtXhp9j+oezSXIqAOV1VbxKRWwBU9Yi4qY6N8hloHiXPuBt8iIJupgA3u7NbcILyzq0bz9jR1Uw8uYa9h3pCqdHHxuQvA/jkrAn8pnW/k55E4ZST0nQdzfj7123p9KPJ42bZcSuffKshj3TKucxA5NSbbQcQKb4KqU7nZhrwAiPTri3KS2D5Ww2z2N3VzaSxo9jd1Z2TFBMcoZSW4s4RoXOy6ngAvrebyz42OZSR4ZRRaQ51Z2JVohed4aSE8VRkHnH9LOJMPD49fzLPB1aw4n4JniqsKi0IFI0bK2WwjgsMrqnqn9zky3+XFEkY5otF6HslLTzHlKA9y2tTKQKgWH8XCsIeii7NpQiYHhEZjfs8i8jpQHH9hRHLQG01QePu3S9uDrmsrtuyj+sX1eUYp99oOxAKdPNSo0O/y3KUzsO9oUDJoHDxcOrC5CZgFJw4Ei+rscfHp43lvY6PYlc6Anz241OZN+nkvLEnxVDtTyVTlXZWXZ714fy5tUwYU8OksaOA/jxWQZtPJuvYolZv6gDXvlLlGocLzaRSKWHOqWNy7rcYvX1ZdkU83z7qzu1n7zOCs/9gTZo4bjl/tm+Pe+X93X4MiqojSL9+8Wkc7O7jkcZtrFjbyiNNbSH35SjRwfre1R9wpDcTip2Jy7mXL/+dd3y5auRySMILq5jQ8mogebndvFxttWNqylpNFfucQkHYQ9GluRQB8xfAz4FZIvIgcBHw1SQbNVCSjuSvBMdigIwaEzNuuozu3iwdXd05On3on+V6x3lBeXFxJgJ8uKdwwknBcRb4zMensP9wTyippELsYPvOzq68s33FietJx4Xkl0HDnFpGVacRCHmfvb6l01H7ZZXH1rex7Jp6zpw2Ntb24qif+o3qK9e2xmYK8MhkNW9amnQkU3OQqrRw03mzeWfHxqI52KJ2nRsX17E7TxVPr/XBFe4/vPCe73CQySpd3X0I/Z5yQfflOIKDdSolvuOIl3H6KxfMjh3Q49yTg4NtIWP4saq4knAgKEVoeZPA6wJF6MpdTRX7nHxB2EPVpbmogFHV50RkPbAEZ3z5I1XdU+S0QSHpSP5KEVR9RZMfFvpRRI2JHopT4vf7X1zIv63Zwts7+j2wguopxalIeOcX6vMO9vkGxeAxfVl47u1dpMoQChl1gjX3Hc7NHQbEr24Cap1CpFNOP8Zo7ID+dDndvVn+3E2pXwpKfPBo8Lajl5o0toYqN3r/3zfsiHV5VhyV551fXMjD61p5a/uBvFkLVPsTmfqDc0pCWZWjq0joD6Q8GvEki7XZxH80EB6sn23eGRLMz2zc4cf2BNW1QdVRcKBf0xIuyx113faoVJBnEp5pnsdkOZ9dzmqqmHCM2x+nPh8qlOJFdhHwhqr+u4j8NvBdEfl7Vd2afPPKZ6gsE0s1jIa9glKxEdkeS+b1l6+Nksk4qomf/dElrFjbyjMbd1A/fRxd3X2sXNvqq4z63OO+eM6Mgi7FxXCEUXlGk3zCxcMbLFM4mYPHu55avRn1B/XoR54/11EblpJFINrmqhSxcT+eLSPf7c2qHcO2zsOx+/d0OV5xhfq2L6Pcu/oDJ39ckYbXuDP/UGBk5Jx0ylmhBfOXeYN0EK/MATgZrL1g3T0FVv5eSn8FLpw3MSRgPNtfMCtAtEJlcLDdtLMrJMyiiSo9hlI+r+gE0POYXFWiAX8gq6liwjHOIWiwx7t8lKIi+xfgHBE5B/gT4H7gx8ClSTZsoAyFZWKpS/xV69tCXkFk+6Ouoz88T2icPvmU2DQlwboYX7lgtq96+PqPG0NxLalAbqxixvc4BnJOqSj9tpSg6s3bOT0mJUx3jHu0AGNPquJgTNr+IFHh4n12sZvcmqcqaDl987xbtCt6/nlza+nuyzKqKsX8qWOpDxSMi7u2Vy47WBGzaWsnb27bn3N9r8zB4jm13PnFhX7uOM/xIGqLiZa5rkkLt18yj+YdB7l64XQWTBvL3S9uZrsbk1NsxeEVrwu2PZ+qqRL5vLzJW7AURbnXiGa/LlT+oNCqY6gKgKQpRcD0qaqKyLXAP6jqv4rI7xU7SUTuB64BOlR1Ycx+Af4e+DxwGPiqqq53913l7ksD96nqX5dyM6eMqmLZNfUlf5lJufeVusTPt9SOpjZfsbY1p6piDjE6lhVrW0MeUCnBT6sPhNyYS0UErj1nBh/uOVRSDEnBa5E7IOdrS5bcrMYAb+84SDqyElGcWfCksaNiC7Tlw//sgFpO6I/KL0Y0FqaQwIm7z1RK+NIn63yBsr51P4tmd4UCI6eOG8XeQz30ua7kt118GgumjfXP+YlbniFuYdTjGumX/24DnYd7QsfEGeHXtOz147DAsduMHV3N//naBaHBN18RsihL5k3kJDf5ZSoloWcxyrEOysHJm1eKotzrxWW/jltZHa/klEPZHTkfpQiYLhH5U+C3gUtEJA1Ul3DeA8A/4ax24rgamO/+XYCzUrrAvf7dwJU4MTfrRORJVX272Ad+1N3HXU83l5QSPMmHIt8SP/qAXLeozq/NHoxbCJYqBkfXXYw4dUO0SNjZM8fzlQtm++3w9Oaep08cUTtIVuHpt3Zw17ULeWdnc44aJo50Ck6bGO9dFl2NBQuLlbIiyGaVhTPH5wi7597exafnTyratmJUp4XZp45hl5v2pWBbAnaRa8+dwZhRVWUJONTJRxa0s0WdKHYd7PaDLLNZ5YHXttCy51Be9Vm0D599excr1rbm1GapSkuOx9Oya+qprur3uEunhe37j/jPj9fOvkyWs2eOp37m+ILBhVFbzbFGuxciOnkbiAtJXPbr6wMGfO8+j0e+suGaYbkUAXMT8BXga6q6U0RmA39T7CRVfVlE5hY45Frgx+okhVojIhNEZDowF9isqi0AIvKQe2xRAQOlf8FJPhRxS/x8D4hXmz1fMCWQUyc+jmAeKnAeyGjyxJvOmx3rR7/2w335BYXC6VNOoaXjo347TlZ5eF0rn184rSQ7TjYLC2eOp2XPodDM/WB3fOXJtDh92N0XTsESRzqdivXIUsLeZAPhzGlj2bz7o1x1nUs+Aag4Qvi2i08rS22WUXh/V1dsKYXgtR1HDMeDsKcvG4p3iXLFWVP5cM+hUDnqh9e1Mm50dciF/caGWTnZIToP97Dy607aoZbdH/HhnkOsXNvKo43buPOLC0OxLm+2HaC5/SALZ4zP+zvy6gVlshpymS8nRqTUY4OTt+qqFPUzxpddFyafqi4pm1Ehyh2vhspqpxQvsp3A3wXet5J/VVIOM4Ftgfdt7ra47Rfku4iILAWWAlSNm5z3C452eNIPRXSJny+hZSneIN5M7+F1rTTvOJhTMtnxCNPQ6i2a6+qMySf7148OIpd+bHJsMCG4rscdH5ESZ2XlXfPNttLSrHjXiBNE+VLsZ+JsMHnwotEXTBvLD555p6TzJHAv0YE5KBCilTjjrpP2MhlE1GOZrPKjV1qKCpeoN1/j1k6WfnoeP3JLKsThOYRkMlknQDVf+4BzZ03g8gVTQipWL/uy9/mefQHiVUAvvbc7NAHpySgb2w/4sS7e5MfLhQbkpJ1p2toZCkTs6XXco1e5RvNis/K4CRrge3VFq2zGTd4GMvvPp6orVF4jiQG9nPFqKK128goYEeki/PtTYA/wIvBfVXVv7ImlE7dq1QLbY1HV5cBygNkLztZSIo29Y45nxtLoA5IvACuf0dAz3K9Y28qfPbEhXMddcyOH2/cfCambPth9iFvvW+NHWXuZZrfvP1KS+iCrMHPCSaHiXINNWhyvKM/T6Y1t+/MeG7zHdMop+SzAC+/uCrlmh7yc8jx1ntdWVTrFH3xqLj96pSXn2GBRsjjOnDaWWaeOyYklyio89VY7WsAwdtN5TjDlqvVtTibpAu30iszdfsk8XmvZGxIuQn+WAO+5i/4mvvf4htjVrdCfiv+1D/aGgnOX/XQjWQ0X+XImPP0NTaXEX4EVmpV73+3G7Qd8NaBXMvuRxm3+qjUuYNT7PXkVXL3UNeVG10cplO4pKcoZrwajxEA+8goYVR0b3SYitThBlvcANx7jZ7cBswLv64B2oCbP9qKcMqrKf3iKrR68B+J4dXxcTIDXpu7ecLBbyICaEm5smOXP0OI8cdJCbORwSAgRjrL2fqAPvd5aejyLCDXpeDfpY2Ug3mkfm+o8orfetyYn3iPIqWOqOWPKKazb4sTK9GWU59/eRXVVaoAuce5gmsnS1BrvIl3MceK9XV1s3v1RbJxMMSE+dlSVP2jHadKq0oKq80x42RGq3HQ4G7b3rzrTkSwBEK5S2n+nYaLZu700KVnVUABw8LfmTbC6e7O+c8LsiSeTcj0j4mblUS826C+ytnH7gdBzWGggrR1T438fWXUqux7LDH+wBvBSx6uhVE+mrHT9qtoJ/E8R+Z0KfPaTwLddG8sFwAFV3SEiu4H5InIasB24GccGVJSWPR/xt89uynlohkqHRx8QL2WJAo80bvOFSChZYEZZsbbV97v37iUYDR1UrUUr+XkIhKKs17Ts9X+gUcOwd3x06479R/j/vnQ2L27q4IUYN9uBIsAn6nIN9cV4d1cXdz3VnJOWPsq+w705qjNv9jwQvBVPRqGxRFVezjXUqS0zEO59pYWm1k639HVYmJ1TN56FM8ez8vXW0PfXl1VeeLfDL+TmPTvRFXNUnbTsmnpq3AwR6ZSzeho7qoq7nmpm6riTmDfpZJp3HOS2i09j7OjqvLbExXNq+eqFc/1Mz/f96kNHvZdVv5pndPCMerEJTkXRd3Z2hQQlFA5P6DzcE4qxat5x8JgERJLjSSVsJ8dbO1OIsuvBiEh1KeeJyErgMmCSiLThpJypBlDVe4Cf4bgob8ZxU/59d1+fiHwb+AWOm/L9qtpcStu8hIlx6euHSod7LJ5Tyw2L61i5ttUPAIyb7SnFC0GtWNvKmpa9dB3p5ZHGbaGBJQVUVTl5tRbOGO/PTD3ViX+chL2gqtNCb1ZDM+SsOj/W2y89nY6DR8sWCEJ8AKMAo6pSZV0LnO/7WF2lyyWYRRnCsTsp90VWc++zWFLMcSdV0XW0r6QFlaqTdBRce4zbnpqqFMu+UA/g2zaCn5nNKjdfMJsZE0bn2EfiUhD1eIb+pRf6z9umnV0Be05/37/y/h5uv2Qe4KSoCdpgPDXXQ6+39pcVCJYpUM1ZmYNb0iDgxVadFupnjvfVfF6V0oUzx+fYYKLXGVVdufQqSY0nlbSdDJXYm0I2mOtiNtfieJU9WuzCqnpLkf0KfCvPvp/hCKCycIyu8bOZodLhQa5fVMdj69tCdhnP08VLS/FoUxuZTO5s0LuXYIxM0NNMcDyIzp01wR8YPA+eUdUp6iaMDrXFtVP7s9TrFtXxf17bEjLOC46X0z/+8v2iqwaPtJsUU0Q4beKY2NxdkpKSjfrlMLN2NLsOHs1xihgoAnz90/No2XMoVMztijOnck6kn7PqBPidNX0co6pSRe+vWFCouP9EVWLZLNxywWxmRoSGNwCubdnre9MpjnptybyJrFrfxmPr2/y8WZ4wCiYw9eKxgmqzZ5t35m3j8lccdVxwcCxUQdMjGPwbxDPUe5kEPEeE4G9m2ReKx73FCYRjTa+SxHgylGwnlaLQSuQLkfcK7AX+XlX/PbkmDZx5k07hW59bMGRWKcUIPvhxni7//ctn+373tWNqYu1L0RiZlPSrw26/9HT/Rx704OnuzboBLmGclVS/auxnG3bk7C8nvUx1Wvj+FxfykqtSy5d1uJyUM19y40s27+oKDdpxK4Qd+49QVYJ96fy5tbyxbb/vDZYvH5viFDlbdk09L7opbKrTwjfcfgavUqVbyiCr1M8cz6NNbfEXLAMFzpyaGzcUrPEDYRXLty4/w39mPJ56q517X+73bntoXSuf/fhUP1BSREKJUJvbD0RWOAXaGHE2Cap7g19NWpxJhVd36DMfnxK6TlxBviADWT1ErzMUJ5yDpcqXmtEnJ3XtQkb+3wcnF5mq/irUoJhtQ4ExNWm+dfkZx/1zj0VvGufpEnVECM6KPb24p4KIxsh88ZwZObVf1rTsDQ3iimP49gbllDh/fa5w+UnjNlaXkCerGJmM8sRv2ormCstXDTKO+VPH8q3Lz+DuFzf7RnvoT9kfUgkpee8hBUwZN4ovnTuTK+un+YXabjpvNq17D/FQ4zb2x+RO6+7N0uymZfH+gkQNyj/fuDNkRyiVlMCCiECpqUqFbGPn1I0PzeA9o7g3QK38+hLqp48LPR9RB4JM1gm+rKlKcdP5s1g4Y3yohPUjjU7EgPdsCnClG1uDOkGuew/1UD99HA+8tiVncAwOmpIS6qeP46bzHLdyb4X+/Du7eGlTBzc2zAqlxsmnJhqKwiGOcseFwVDlN23tpLp2xseSun4pNph/BBaVsO2EpFJ600LR/9HVR9QN9L9/+Ww/ueX9v/qQ3oyytmWvHxOzZN7EUKYACMeZCLBodm3Iy6oS7shZisez1FQ5rr73vfphbAmBKG9s20/TVsfAHTxaKV1IASCw82A3y19p4Uevtvirlg3bNxSs0qk4ZSH6XDtF0HbWtLUzJ3vCvkNh24KjhvRWC07izHy5zT45p9b3NEuJk2xy064ux8U8JSycOT50vJeIEfrTwrz8/m7f9jV9Qm4uN4/evizb9h3m+kV1ObZBxfmeenqdCqanTzrZqTXTl6Vt/xH/mb+yflpsUGJQ3bth+wE27WrmwduWOGUjMmFnFq9voiuhY2Ewgg4HOi4cb+HprHClfANoiRSywVwIfAqYLCL/ObBrHI7x3aB0vWmxh3zxnPiU53GrD28g7u3rr+/ynSs+xr2rPwjlX/qzxzcwaewo6qePo5A/bkahqXU/1UVckAUnBcpTb7aHViRzTh3D9v1HnEJk6dwyy4X4/MJp3PH5M/3BafWmjoJCySsMdmPDrLzHFCMdELZZz4vCJVuCpGpzBYKAH0u0Ym1raOYfh+Dkf/NWoLVjavizJ3JzzAmOW/HCGeP5ibb67frRqx8WLBjW0RXOl7br4FFfPZUC6qePyytgFMeG9+sP9vDZj0+lOu30UTrlVL786oXOJCCr6v9fqtu/pyrzhIl3TpwzS1bVt9uVqyaK+40NVtDhcLGnOP2rA3OnLIFCK5ga4BT3mGBMzEHghqQaNNwoRW9aatnZu552XG5f+8DRm3vFnEZVp3LiPLyZ8KNNbX4Fxqgq5p2dXbCzKyfNjKfWCQ6jmayyeG4tTW655DhEYMyoqtB5NWnh7246F8CfpebzhfIG2OC9eMGRXn9s33+E32zbT19Gc1ZdHj0ZpaOr24n/KVOLlxJYNHuC74U1EIJqq94+pzBZOiVFV2CeO65nYH5pU0dIpSc4MSx9GccN7cVNHaEknpmsM7h/5uNTQgXD7l39AZPHjuLFTR3+sVWus8amXc3+isdpg6MWS7m6tujIElSZfebMKax+bzcrX2/109d4jgCpAg41ccT9TrxJlZfVGRzX/Tu/UJ+TCaAQK9a2Olku2g/43nTBAM/BGOiHSmhEMRbPqaW3s/29pK5fyAazGlgtIg8M1dovQ4FS9KahuJbeLD98/r2cALc1Lf3FmPqy6s9sv3LBbJZdU8//+MW7IZvAnIljuOiMSax0XT+9bK+loDgz5E+dPjGUr6txS2fBrIBVKWHzrq7QoHjurAmh1VZfjIU87V6zuirF+XNPDX3mlLGjaNrqDPbB2jhfuWA29TPG5y0QJjiGYm9k8soBjx1dzZvb9oe8vIJ4doRQ+1JC3YTRBdPwB+07UdUckCNcqlJw28Xz+HnzTrbuPeyq8JwUK56NIcqMCSex48BRV02Z5ZfvduQck81qTrnlaOp/wREuXgodT/A/F8hZJsDCuvF5c71lMlmO9mb8VYdGCtyl3ADgQsktg+T7nXQe7gm16cxpY0tKVusRl2k8LsDzeA/00fsFys6FdrzQniPl1fwug1JsMPeJyI2quh/8aP6HVPU/JNWo4UZUNZAv75lXH/1Xm/ewtmVvKEJ/ybyJoVlwVuHPnthA695DPPDalpwVzNJLTvcHDy/tSzabjS2gFUfQHdUjC1SJICjpdIpMNhvyqOrNKhvbD4bO6Q58YPQ+vTxXQZXQnU9u9I9PiZN769b71vDp+ZNDtXE6urr5SzcHWzQ1TlXaSTPiBSo6A+os7vj8mYAz6OQTMEDO6mXx7Als3p1bKjodiG/xAgm7jvT60fFBolmgbzpvNnd8/kxmTzy5323ZNdLnK5FQP8MxmHvCJypYBaip7k/u2et6YUWvNaq6P9IeYNu+w/RlsqGBvLoqxYXzJtLcfjBHOHr766eP47UP9sY/K1ll5oTRBQdLr4aRlzk5ToUWDBzOKrzVdiC2Km0+FXNcpvGoS3/ShvN8bfPudyjlBjvelCJgJnnCBZxofhGZUuD4E5pCec9++Px7/GrzntgI/cVznJQbwcE0q/2xBeD88OdMHMPSS073E2AG3Zyb2w/Q0dXNgcM9IQ8rCNsdwLEd1E8f57fH45pPTGf+1LGOMHiqmUy2X4CowuGeTOh+p4w7ya+GGHW79tQcgK8S6gkF2Dn/9/Zl6YjMyn/5bgdNWztDs/A9Xd1MGjvK93TyrlSdFj9GAnILWwU9ryDXGpXP5vPZM6fyy3c7yKqTFt/7nt7ecTC0CksJLP30vJAX1XVujrS7nm72bRmeeiwuCLLKdXe+bMGUkFNH8B4unu/kDQO4oWEWAuzu6s4Rpl+9cG5sDEpKHBWUF3Trtc1bzbbuO8y5syb43/9dTzeTVc0p2RC0PeWrhBkXnxWXnj/42/DKRsQV88o3QEe9KM+bW+unEIq6bCdBKcJjuNhjkqAUAZMVkdluFmVEZA7JFTUc9hTKe/adKz7Gui37YiP0veCvK86cynPv7PLtzN4MWtwZ8N/+1rk5syQg9JBft6guNEuvcmfgwQy9mUyW+3+9JWf2+9Sb7fzk9k/lVXcFSQEvvLOLV97fHUr4FxwYvJxnfVnNVU25G9LpFFPGnURK+hMxerXog9f0Bozm9gN+2wQn1XywT5bMmxjKmVaOG7RHTVqYPHZUjjEb4NcfhGNLbj5/dshRwZvJeq7nnnqs83BP7IAqwE3uPUSTQnp9Nqo65QuXaLmFF97ZFbJF3ffqh35bgkb+YGLL7z6+ob+GTFb59QfO5+48eJTfuXBu6DlOSX/AbDolXLZgCi9t6uCh11t5LFI62PuOogGZz2zckbf+S/C34XmqBUtPFBqgvWt6XpQPvLaFpq2djnu1SMES5JWgFOExXOwxSVCKgPke8KqIrHbfX4KbHt/IpdDDFHXZDEboB2dC6ZSgbpqWqipH9QXkHSWjD7lnTA9WDuw83EM2IE0yCtkYfVpGHWP99YvqQh4+UXybRJ4fVlwEt5eo0Ksff+cX6mluP8DDjdt4/u1dpNy0J6rOTDvYd9EEoFXplN9/QXWQ1883NsxihetqW45wEYErz5zKNy49HcBXQXrfU1QAVKX6V09RFVC+Z2HxnNrQzFtx1GNNWzvZvv8IVekUfX1Z3wAfzNUVjZfqPNzDf/vS2ZGVr4a8tLwUMJ53YtPWTh5tautfkQg5gjTa9mXXON9V0OYUHVSD31E0jtfxZMyPZ/D31Il3Pe1kh/JUq4UGaC/TeKhvMs7DGbciOhYGUvZjMOJbhgql1IP5uYgsApbgTKj+WFWPrZLTCKbYw+QNQtHKeMEfh2SVm893ckZt33+Eh17PzVcWJPqQX7eojvoZ40P676atnaG8TlUpQMS3YwTHYAncx2Pr23i4cZtznMCM8Scxc8Jo1m/b7yfJ9NLCX/tPrzJl3EncfunpORHcnv0gmqfqe49v8FO5ZLIBA3xEKgSFaCar3HT+rJDqK65a6Kr1bb49qFSuOHMqy3+3wX8f910GBe/8KafkvVahZyEnAWPA+F+VEs6u68+55a1+IL/Q8tR56sZHBb20vBRBXr2g4OpU3HNffn93jodX1Ejtty9PieTgdyQRod4VU1wuSufhHl/Q9QTivarSKS772GQmjR1V0Kkg2DfplLjPd24apoEO8MdS9qNYfMtQKRBWaUpNdpkBOoCTgLNEBFV9OblmDW9KCZYqNtv1jP+emqmcGRL0DwZe5cDFc8J5nTw7RgYlnXZiHfoyGloReG28zs2Z9kjjNnYcOMqug93+LF5wPMn6Dd8HeOHtXXzx3Bl+pHs6LfxWHo+j6OLCex8VptH+WRiI+H40oA7xbAzXL6rjwduWcNdTzTkJMQslnlz93u6QXSH6PS2e42QF9u73nZ1d3Lz8NR5aemHeGJC47dEEjAohAVo/c7wfVFnIaA2ESzu4eeTA8Vpq33+ErIZn8tG+/Malp/MNd1IQTFCZbwKUyWT9CVBwQIwO8Aq+O3UwW3g+QlH/Abfonr4sz729i1HVqZCtLa6vo32TLw3TQAbxQurvYxEKI9kJoJSsyLcBf4RTl+UNnJXMa8BnEm3ZCUa+mdBAZkjBwaC71wnGjP4Q7n5xs+9ZpFnlt87PTZgYvPaalr2+WgR1YlQERyAFPcnA8UaLJsm8PiAwg/eycMb4HCO84KyK3ty2n+8+vsE/N189nZ6M+lfoceNSPNtA/cxwGQChcFbjTKa4OqV5R9iTrjcTv7IsRNxgGJxIBOu/B/PQAXkHfi8+CMirToxbnQSfNYgf8PJNgArd072rP/AdEPKtvvOd7wmFfPbKQteITgjypWEql6RsKSPZCaCUFcwfAecBa1T1chH5OPD9ZJt1YpJvJlTuDGnJvIlUpRwjtwKPNrXlrB6CLsUiTtR4PiNs8PigTt5TdT3XvLNg2vw+dwCGXAO1p2uH/jIBly2YwoubOvzB6dHGbax0VwjePWza2ZVXUAQHo+sX1fFo4zY/3UrcOcFkl6UMHFHPpep0fDbgYkSdF6LqQw9fWKRToEpfVkMDf/C+vNLXUXVidPJQ6JmKG/DiykQUu6eXAoGf6XRpA3KwXQumjfVXzqV8N/nUTJUSDOXYUspReY1kJ4BSBMxRVT0qIojIKFV9V0QWJN4yY8BEjdx9fbnBnZ6qZ/krLSH9fNRQH/yRxP24Vqxt5b5XPyzYnqCBPDhwPbNxR9jLyXXDXdOy1x8oIX6FEOeK/Im68byz42BoMFo8p9avZ1I7pibHBfhzZzkG/WBK+OiAEFe+GuDhda1MHXdSKJtyvnPyUUw9Eu0zyK0PVD8jvErrOHg0NGBF76lY2wo5J8QdH3c9b8XrfTc3LC4tIDNIUEUbXcnF9Xe+fqykkb2UyV65Kq9Ktm+oUYqAaRORCcATwHMi0kmJJYyN5Cg2SFy3qI5Hmtx4C5zgznVb9oXqdDg5pZzje3pLiz2IDlRxMRvQP+BHi0EFB65o4aegAIwWmorO6qKuyF5tEMittR5tdzBZ6GULpoTuM6rjX7G2NbbGvOe5lO+7KXWAKaYeCdk13BVMdDZ/03mzebOtP5rdy1Yc93yU0rZyZ+px14sKqUK2k2J4n1+OII7240DsJAM1vA9E5XWsdpyhSileZF92X94pIi8C44GfJ9oqoyAlD2ABT6zowx51t02lwoN4KT+S6DXS4qhCPH1/tBhU3MAVNxAunpNbaCpuEFy59EJWrW9DIOQUUQgvcDPOlhMX4BfNZB2X5idKOQNMMfVInK0m2l/BWBDPa9A7t1Dbegq0rdQBr5Dhu5Kz8nIEcSXUTMdieB/JKq9yKShgRCQFvKWqC8HPT1YyInIV8Pc42ZfvU9W/juz/E+DWQFvOBCar6j4R2QJ04Xiw9alqAwZQ+uAfXFkI4RQa0RiJYH324P5CP5K4axSrFBhnhB3oABe3oiplUIiel+8+17TkZrKOrgTjCPZLNGgwri3FBuK4PotSaEUVJFqvplDbSqFY3FelZuXlCuKBfq63amnff2TAhveRrPIql4ICRlWzIvJmMJK/VEQkDdwNXAm0AetE5ElVfTtw/b8B/sY9/gs4MTb7Ape53GJuciln8PdcRoN5z6C0eJ1SBr5C3kjHm4GqJvLdp+dK3NOb9csVl3rtS+ZPdiLs89i3om04Xn0Wjb+J2rLKpVD/VTK2YyCCuFwKBfOWuwoZqSqvcinFBjMdaBaR1wE/66aqfrHIeecDm1W1BUBEHgKuBd7Oc/wtwMoS2nPCcyyDf/SYYsbHY9UdH88AsoGqJgqtoqJus8WuHZfBYCi5nkbjbwaivokraRy9tyRiO5IetOOCefO57hulUYqAOQW4JvBegB+UcN5MYFvgfRtwQdyBIjIGuAr4dmCzAs+KiAL3quryPOcuxU1dM3t2cRXBSKESg3/SHO8AsnJVE6UIv2AfFlP/Qf8gFcxgUMpAfrwEcZxNp5wI91K/0+EY2xHnmDDU2zzUKUXAVEVtLyIyuoTzJGZbvhC3LwC/iqjHLlLVdjdz83Mi8m5c9gBX8CwHaGhoKCPrlJE0gzHIlCpUByL8Srl21OvLyypQbJV3vAWx5xBR7ueW+p0eT0N3pYSz2U4qT6GSyd8E/hCYJyJvBXaNBX5VwrXbgGBd2zryuzffTEQ9pqrt7v8dIvI4jsrN0tMMI4ayN01Swm8gg9RgzfYH8rmlfqfHa7CutHAe7FX/SKPQCmYF8AzwV8Adge1dkZVGPtYB80XkNGA7jhD5SvQgERkPXAr8dmDbyUBKVbvc158D7irhM40hRKmDzGAk+ktS+JU7SA2WIB7I55YjOI7HYD0cVXEnEqLlFsoo5+Iinwd+iOOmfL+q/qWI3A6gqve4x3wVuEpVbw6cNw943H1bBaxQ1b8s9nkNDQ3a2NhY0XswkqWcGWilBdFQymA7WPc2lPpgIHjPjyckR1KiyOOFiDQlFQaSqIA53piAGX7c/eJm/vbZTWTVCdT8z59bEFt9cCRnnK00J1pfDXchOdgkKWBSSVzUGBk0be3k7hc3F42OPxY8NU1aCntbxalCjHiS7qvj8VyUw+I5tXzr8jNMuAxBSq0HY5xgDHQWXGg2GbevVJ1+IXvBcJzBJtnmJG06J9rqyDg2TMAYsQzEeFpo8CmW7XagEffDccBLus1JenCZUd0oB1ORGbGUqroKUkg1Uwm1TZwqZDiqzirZ5nzqqlLURgNRdQ3kuTBOXGwFY8QykFlwIdVMUmqboRxrk49KtflYVkIDPdeCEY1yMAFj5KXcOIZCg09SA9NwHPAq1eZjUVcdy7kWjGiUigkYo2IUM1wnNTCVe92h4BRQib44lpXQsa6ihkIfGkMfi4MxClKp0r9DheHSzlI5loF+oOfG9SHkFkIzhgdJxsHYCsbISyVL/w4Vhks7SyXfSqjcTNHlEO3DVevbeGx924gR2kblMC+yCjGUgs8q1ZZyvJ2Gi3fRUGlnks+LNzH422c3cet9ayr+GdE+FBh2nnzG8cFWMBVgKKldKtmWcvT0SSS2TELPP1hOAcF7ARJ9XpJepUX7EGDV+rZh5clnHB9MwFSAoaB2qUQt8SjlDsbFVC7lJrZMahA+3l5Q0Xu5blFdos/L8XDdjvbhcPPkM44PJmAqwGDHYlSylniUSg7G5QjioSC0K0X0XgQSfV4GY5VmrstGHCZgKsBgx2IMl1ri5QjiwRbalSR6L9ctquO6RXWJPi824BtDAXNTHgEMp5oYg22DGSxG0r0YIwurB1MiJ6qAARvADMMYGBYHYxTFVCKGYQw1LA7GMAzDSIREBYyIXCUim0Rks4jcEbP/MhE5ICJvuH/LSj3XMAzDGNokpiITkTRwN3Al0AasE5EnVfXtyKGvqOo1AzzXMAzDGKIkuYI5H9isqi2q2gM8BFx7HM41DMMwhgBJCpiZwLbA+zZ3W5QLReRNEXlGROrLPBcRWSoijSLSuHv37kq02zAMw6gASQoYidkW9YleD8xR1XOAfwSeKONcZ6PqclVtUNWGyZMnD7SthmEYRoVJUsC0AbMC7+uA9uABqnpQVT9yX/8MqBaRSaWcaxiGYQxtkhQw64D5InKaiNQANwNPBg8QkWkiIu7r89327C3lXMMwDGNok5gXmar2ici3gV8AaeB+VW0Wkdvd/fcANwDfFJE+4AhwszqpBWLPTaqthmEYRuWxVDGGYRgnMEmmirFIfsMwDCMRTMAYhmEYiWACxjAMw0gEEzCGYRhGIpiAMQzDMBLBBIxhGIaRCCZgDMMwjEQwAWMYhmEkggkYwzAMIxFMwBiGYRiJYALGMAzDSAQTMIZhGEYimIAxDMMwEsEEjGEYhpEIJmAMwzCMRDABYxiGYSSCCRjDMAwjERIVMCJylYhsEpHNInJHzP5bReQt9+/XInJOYN8WEdkgIm+IiJWpNAzDGGZUJXVhEUkDdwNXAm3AOhF5UlXfDhz2IXCpqnaKyNXAcuCCwP7LVXVPUm00DMMwkiPJFcz5wGZVbVHVHuAh4NrgAar6a1XtdN+uAeoSbI9hGIZxHElSwMwEtgXet7nb8vE14JnAewWeFZEmEVma7yQRWSoijSLSuHv37mNqsGEYhlE5ElORARKzTWMPFLkcR8BcHNh8kaq2i8gU4DkReVdVX865oOpyHNUaDQ0Nsdc3DMMwjj9JrmDagFmB93VAe/QgEfkEcB9wraru9bararv7fwfwOI7KzTAMwxgmJClg1gHzReQ0EakBbgaeDB4gIrOBx4DfUdX3AttPFpGx3mvgc8DGBNtqGIZhVJjEVGSq2ici3wZ+AaSB+1W1WURud/ffAywDJgL/LCIAfaraAEwFHne3VQErVPXnSbXVMAzDqDyiOnLMFg0NDdrYaCEzhmEYpSIiTe7EvuJYJL9hGIaRCCZgDMMwjEQwAWMYhmEkggkYwzAMIxFMwBiGYRiJYALGMAzDSAQTMIZhGEYimIAxDMMwEsEEjGEYhpEIJmAMwzCMRDABYxiGYSSCCRjDMAwjEUzAGIZhGIlgAsYwDMNIBBMwhmEYRiKYgDEMwzASwQSMYRiGkQgmYAzDMIxESFTAiMhVIrJJRDaLyB0x+0VE/sHd/5aILCr1XMMwDGNok5iAEZE0cDdwNXAWcIuInBU57Gpgvvu3FPiXMs7NYcP2A8y9498rdg+GYRjGwElyBXM+sFlVW1S1B3gIuDZyzLXAj9VhDTBBRKaXeG5eTMgYhmEMPkkKmJnAtsD7NndbKceUci4AIrJURBpFpDFz+MAxN9owDMOoDEkKGInZpiUeU8q5zkbV5araoKoN6THjy2yiYRiGkRRVCV67DZgVeF8HtJd4TE0J5+Zly1//x7IaahiGYVSeJFcw64D5InKaiNQANwNPRo55Evhd15tsCXBAVXeUeG4OZ88cb8LFMAxjiJDYCkZV+0Tk28AvgDRwv6o2i8jt7v57gJ8Bnwc2A4eB3y90blJtNQzDMCqPqMaaNoYlDQ0N2tjYONjNMAzDGDaISJOqNiRxbYvkNwzDMBLBBIxhGIaRCCZgDMMwjEQwAWMYhmEkwogy8otIF7BpsNsxRJgE7BnsRgwBrB/6sb7ox/qinwWqOjaJCycZaDkYbErKG2K4ISKN1hfWD0GsL/qxvuhHRBJzvTUVmWEYhpEIJmAMwzCMRBhpAmb5YDdgCGF94WD90I/1RT/WF/0k1hcjyshvGIZhDB1G2grGMAzDGCKYgDEMwzASYUQIGBG5SkQ2ichmEbljsNuTBCIyS0ReFJF3RKRZRP7I3X6qiDwnIu+7/9cGzvlTt082ich/CGxfLCIb3H3/ICJxBd6GNCKSFpHfiMjT7vsTtR8miMijIvKu+2xceAL3xR+7v42NIrJSRE46kfpCRO4XkQ4R2RjYVrH7F5FRIvKwu32tiMwt2ihVHdZ/OOn8PwDm4RQqexM4a7DblcB9TgcWua/HAu8BZwH/A7jD3X4H8AP39VluX4wCTnP7KO3uex24EKdy6DPA1YN9fwPoj/8MrACedt+fqP3wv4Hb3Nc1wIQTsS9wSqp/CIx23/8E+OqJ1BfAJcAiYGNgW8XuH/hD4B739c3Aw0XbNNidUoFOvRD4ReD9nwJ/OtjtOg73/VPgSpzMBdPdbdNxgk1z+gGnts6F7jHvBrbfAtw72PdT5r3XAS8An6FfwJyI/TDOHVQlsv1E7IuZwDbgVJwA8qeBz51ofQHMjQiYit2/d4z7ugonE4IUas9IUJF5D5ZHm7ttxOIuTT8JrAWmqlMFFPf/Ke5h+fplpvs6un048UPg/wWygW0nYj/MA3YD/8tVF94nIidzAvaFqm4H/n+gFdiBUx33WU7AvohQyfv3z1HVPuAAMLHQh48EAROnHx2xvtcicgqwCviOqh4sdGjMNi2wfVggItcAHaraVOopMduGfT+4VOGoRP5FVT8JHMJRg+RjxPaFa1u4FkfdMwM4WUR+u9ApMdtGRF+UyEDuv+y+GQkCpg2YFXhfB7QPUlsSRUSqcYTLg6r6mLt5l4hMd/dPBzrc7fn6pc19Hd0+XLgI+KKIbAEeAj4jIv/GidcP4NxDm6qudd8/iiNwTsS+uAL4UFV3q2ov8BjwKU7MvghSyfv3zxGRKmA8sK/Qh48EAbMOmC8ip4lIDY7x6clBblPFcT05/hV4R1X/LrDrSeD33Ne/h2Ob8bbf7Hp+nAbMB153l8ldIrLEvebvBs4Z8qjqn6pqnarOxfmuf6mqv80J1g8AqroT2CYiC9xNnwXe5gTsCxzV2BIRGePew2eBdzgx+yJIJe8/eK0bcH57hVd3g22UqpBh6/M4XlUfAN8b7PYkdI8X4yxH3wLecP8+j6MDfQF43/3/1MA533P7ZBMBTxigAdjo7vsnihjqhuofcBn9Rv4Tsh+Ac4FG97l4Aqg9gfvi+8C77n38HxwPqROmL4CVOPanXpzVxtcqef/AScAjwGYcT7N5xdpkqWIMwzCMRBgJKjLDMAxjCGICxjAMw0gEEzCGYRhGIpiAMQzDMBLBBIxhGIaRCCZgjEFDRKaJyEMi8oGIvC0iPxORjw12uwBE5KsiMqNC15ogIn9YiWsN8PPnBjPsFjjmK4H3DSLyD8m3zhjJmIAxBgU3iOtx4CVVPV1VzwK+C0wd3Jb5fBUn5UgOIpIu81oTcDLRDmXmAr6AUdVGVf1Pg9ccYyRgAsYYLC4HelX1Hm+Dqr6hqq8AiMifiMg6EXlLRL7vbpsrTs2TH7l1P54VkdHuvv/kroLeEpGH3G13ish/8a4vTp2QuSJysoj8u4i86W67KdgwEbkBJ9jsQRF5Q0RGi8gWEVkmIq8CN4rI1932vSkiq0RkjHvuVBF53N3+poh8Cvhr4HT3Wn8T+ay54tRy+d9u2x8NXOuzbhLLDeLU+hjlbt8iIj8QkdfdvzPc7Q+4bfeu/VG0093Pe0VE1rt/n3J3/TXwabeNfywil0m41s4TbvvWiMgnAv17v4i8JCItImICyQhhAsYYLBYCsQkrReRzOKkrzseJVF8sIpe4u+cDd6tqPbAfuN7dfgfwSVX9BHB7kc++CmhX1XNUdSHw8+BOVX0UJzr+VlU9V1WPuLuOqurFqvoQ8Jiqnqeq5+CkJPmae8w/AKvd7YuAZrdtH7jX+pOY9iwAlrttPwj8oYicBDwA3KSqZ+Mktvxm4JyDqno+TqT1D4vcb5AO4EpVXQTc5LYXt42vuG38n5Fzvg/8xm3fd4EfB/Z9HPgPON/VX4iTL88wABMwxtDkc+7fb4D1OIPYfHffh6r6hvu6CUe1A06qlAfFyaDbV+T6G4Ar3FXAp1X1QIntejjweqG7EtgA3ArUu9s/A/wLgKpmSrz2NlX9lfv633DSAi3Audf33O3/G6eglMfKwP8Xlth+gGrgR267H8EpPFWMi3FSr6CqvwQmish4d9+/q2q3qu7BEV5DRcVpDAFMwBiDRTOwOM8+Af7KnU2fq6pnqOq/uvu6A8dlcGb2AP8RuNu9ZpM42V77CD/jJwG4g/ZiHEHzVyKyrMQ2Hwq8fgD4tru6+L537QESzdeUL216vnO81/79ujaumpjz/hjYBZyDowaMOyZKoTTt+b4PwzABYwwavwRGicjXvQ0icp6IXIpTOe8PxKl9g4jMFJEpea6DiKSAWar6Ik4hsgnAKcAWHDUVIrIIp1YIrnfYYVX9N5wiVYtiLtuFU5o6H2OBHa5K6NbA9hdwVVkikhaRcSVca7aIeKuQW4BXcZI2zvXsK8DvAKsD59wU+P819/UW+oX2tTirlSjjgR2qmnWv6TksFGrjy7j3KCKXAXu0cC0iwwBstmEMEqqqIvJl4IcicgdwFGeA/I6qvi8iZwKvORNxPgJ+G2eGHEca+DdXbSPA/1TV/SKyCvhdEXkDp6yDp246G/gbEcniZJ79Zsw1HwDuEZEjxKug/hynouhWnJWQNzj/EbBcRL7mtvebqvqaiPxKHFfhZ2LsMO8Avyci9+Jkvf0XVT0qIr8PPOKuxtYB9wTOGSUia3Emibe4234E/FREXscRdMEVl8c/A6tE5EbgxcAxbwF9IvKme++/CZxzJ07VzLeAw/SnbDeMglg2ZcMYRMQpf/2062xQ6jlbgAbX7mEYQxZTkRmGYRiJYCsYwzAMIxFsBWMYhmEkggkYwzAMIxFMwBiGYRiJYALGMAzDSAQTMIZhGEYi/F+kbH5LsjpvBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iS0Vk6e7du2M5tUr06tWL2bNn88gjjzB//nyaNm0a9bj333+fjIwMzjvvPFatWhV1jYysrCxWr17NoEGD6NOnD2+88QY5OTkkJSVx1llnsWbNGhYvXsyDDz7IvHnzmD9/fiiFe9DatWtJT0+nS5cuiAg//OEPQ/tmzZrFM888Q58+fUIZd7du3VrqfI2SuiZapt3i4mI2bNjA3LlzmTJlCqNHj2bfvn0V/qx+//vfM3PmTHJzc7n99tt58MEHKzzeGJNYiaxx5ALtw163A7Z7PUZEknGCxtuq+lHYMTuDzVki0gbYFe3NVXUiMBGcXFUVFbSimkGiREupHlyoKWjz5s08//zzLFmyhLS0NG677bZQmvRwqspll13GlClTyuy78MIL+fTTT0lOTmbIkCHcdtttlJSU8Pzzz5c5tryU6qrK1KlT6dq1a7n3065dO+bOnRt6nZuby+DBg6Mel5mZSXJyMunp6XTt2pUNGzaE1gaJtHv3br7++utQDemGG26otIZijEmsRNY4lgBdRCRdRFKAG4HpEcdMB25xR1dlAgVuQBDgNWCNqv4uyjm3ut/fCpRtcK8Fykup3rhxYw4cOADA/v37adSoEU2bNmXnzp18+umnofPDj8vMzGTBggWh0UaHDx8ONQlddNFFvPDCCwwcOJCWLVuSl5fH2rVr6dGjR6nydOvWjc2bN/PNN98AlApCQ4cO5aWXXgrVKoLNX+GGDh3KrFmzyM/PJz8/n1mzZjF06NAyx1199dXMmTMHgD179rB+/fpQf0s0aWlpFBQUhO7ns88+s0SGxlSzhNU4VLVYRMYA/8QZYTVZVVeJyL3u/gnATJyRVRuBw8Dt7umDgB8BK0TkK3fbY6o6E3gGeF9E7gS2Atcn6h4SKVpKdYC7776b4cOH06ZNG+bMmcN5551Hjx496Ny5M4MGDQqdH3nc66+/zk033cSxY8cAeOqppzj77LMZMGAAO3fu5KKLLgKgd+/etGrVqkztIjU1lYkTJ3LFFVfQokULLrjgAlauXAnAE088wQMPPEDv3r1RVTp16sSMGTNKnX/aaafxxBNPhGoOY8eO5bTTTgt9369fP6666qpQgOnevTt+v5/nnnuO5s2bA07taO3atRw8eJB27drx2muvMXToUF599VWuu+46fD4faWlpTJ48Od6/DmNMDCytuqkz7HdqTHxZWnVjjDFxYYHDGGNMTOp14KgPzXT1hf0ujak69TZwpKamkpeXZw+cOkBVycvLIzU1tbqLYky9kMh5HDVau3btyM3NpSZODjSxS01NpV27dtVdDGPqhXobOIIT0IwxxsSm3jZVGWOMOTEWOIwxxsTEAocxxpiYWOAwxhgTEwscxhhjYmKBwxhjTEwscBhjjImJBQ5jjDExscBhjDEmJhY4jDHGxMQChzHGmJhY4DDGGBMTCxzGGGNiYoHDGGNMTCxwGGOMiYkFDmOMMTGxwGGMMSYmFjiMMcbExAKHMcaYmFjgMMYYExMLHMaYhMjOyeflORvJzsmv7qKYOEuq7gIYY+qe7Jx8bp6URWFxgJQkH2+PzqRvx7TqLpaJE6txGGPiLmtTHoXFAQIKRcUBsjblVXeRTBxZ4DDGxF1m5+akJPnwCyQn+cjs3Ly6i2TiyJqqjDFx17djGm+PziRrUx6ZnZtbM1UdY4HDGJMQfTumWcCooxLaVCUiw0RknYhsFJFHo+wXEXnR3b9cRDLC9k0WkV0isjLinHEisk1EvnK/RiTyHowxxpSWsMAhIn7gZWA40B24SUS6Rxw2HOjift0NvBK273VgWDmX/72q9nG/Zsa14MYYYyqUyBpHf2Cjqm5S1ULgXWBkxDEjgTfVkQU0E5E2AKo6D9ibwPIZY4w5AZUGDhFJ97ItijOAb8Ne57rbYj0mmjFu09ZkEbFGVGOMqUJeahxTo2z70MN5EmWbnsAxkV4BzgT6ADuA30Z9c5G7RWSpiCzdvXt3JZc0xhjjVbmjqkSkG9ADaCoi14btagKkerh2LtA+7HU7YPsJHFOKqu4MK+OrwIxyjpsITATo169fZcHIGGOMRxUNx+0KXAk0A/5f2PYDwF0err0E6OI2a20DbgRGRRwzHafZ6V1gAFCgqjsquqiItAk75hpgZUXHG2OMia9yA4eqTgOmichAVV0Y64VVtVhExgD/BPzAZFVdJSL3uvsnADOBEcBG4DBwe/B8EZkCDAZaiEgu8KSqvgb8RkT64DRpbQHuibVsxhhjTpyoVtyKIyItcWoYnQgLNKp6R0JLFkf9+vXTpUuXVncxjDGmVhGRbFXtF7ndy8zxacB8YDZQEu+CGWOMqV28BI6GqvpIwktijDGmVvAyHHeGpfUwxtQmtohUYlU0HPcATge0AI+JyDGgyH2tqtqkaopojDHe2SJSiVfRqKrGVVkQY4yJh2iLSFngiK9K+zjCM9aGKQByVLU4/kUyxpgTF1xEqqg4YItIJYiXzvE/ARnACvd1L+BroLmI3KuqsxJVOGOMiZUtIpV4XgLHFuBOVV0F4KZGfxj4JfARYIHDGFOj2CJSieVlVFW3YNAAUNXVwHmquilxxTLGGFNTealxrBORV3DW0wC4AVgvIqfgjLIyxhhTj3ipcdyGk0vqAeBnwCZ3WxFwcYLKZYwxpoaqtMahqkdw1ryItu7FwbiXyBhjTI1W0QTA91X1ByKygiiLK6lq74SWzBhjTI1UUY3jfvffK6uiIMYYY2qHcvs4goslqWqOu6mL+/0uYG8VlM0YY0wNVGnnuIjchbPG+J/dTe2ATxJYJmOMMTWYl1FV9wGDgP0AqroBaJXIQhljjKm5vASOY6paGHwhIklE6Sw3xhhTP3gJHP8WkceABiJyGfAB8LfEFssYUx2eXfwszy5+trqLYWo4LzPHHwXuxElyeA8wE5iUyEIZY6rH2r1rq7sIphbwEjgGA2+r6qsJLosxxphawEvguA2YICJ5wHz36wtVtTUZjTGmHvKScuQWABFpC3wfeBlo6+VcY4wxdY+XFQB/CFyIs4DTHuCPOLUOY4wx9ZCXWsMLwDfABGCOqm5JZIGMMcbUbJUOx1XVFsAdQCrwtIgsFpG/JrxkxhhjaiQvKUeaAB2AjkAnoCkQSGyxjDF1QXZOPi/P2Uh2jo2lqUu8NFV9Efb1R1XNTWyRjDF1QXZOPjdPyqKwOEBKko+3R2faOuB1hJdRVbbuhjEmZlmb8igsDhBQKCoOkLUpzwJHHeEl5YgxxsQss3NzUpJ8+AWSk3xkdm5e3UUycWJzMYwxCdG3Yxpvj84ka1MemZ2bW22jDrEahzEmYfp2TOO+i88CSFgnuXXAVz0vEwBbAnfhjKgKHa+qd3g4dxjwB8APTFLVZyL2i7t/BHAYuE1Vl7n7JuMsW7tLVXuGnXMa8J5bni3ADyz9iTE1VyI7ya0Dvnp4qXFMwxmCOxv4e9hXhUTEj5OeZDjQHbhJRLpHHDYc6OJ+3Q28ErbvdWBYlEs/Cnyuql2Az93XxpgaKloneW24timflz6Ohqr6yAlcuz+wUVU3AYjIu8BIYHXYMSOBN1VVgSwRaSYibVR1h6rOE5FOUa47EidjL8AbwFzgRMpnjKkCwU7youJA3DvJE3ltUz4vgWOGiIxQ1ZkxXvsM4Nuw17nAAA/HnAHsqOC6rVV1B4Cq7hARW8bWmBooOyc/1DGeqE5y64CvHl4Cx/3AYyJyDCgCBFBVbVLJeRJlW+SSs16OOSEicjdO8xcdOnSIxyWNMR4F+x6OFQXw+4TxI3uGOsnjrW/HtFDACA9WFkQSx8sEwMYneO1coH3Y63bA9hM4JtLOYHOWiLQBdkU7SFUnAhMB+vXrZ2ukG1OFsjblcawogALFAWXstJV0Pb1xQh/m1lFedcrtHBeRbu6/GdG+PFx7CdBFRNJFJAW4EZgeccx04BZxZAIFwWaoCkwHbnW/vxWn894YU4Nkdm6O33e8QSGgmvCOa+sorzoV1TgexGnq+W2UfQpcUtGFVbVYRMYA/8QZjjtZVVeJyL3u/gk465ePADbiDMe9PXi+iEzB6QRvISK5wJOq+hrwDPC+iNwJbAWu93Cfxpgq1LdjGuNH9uSJT1YQUEjyScI7rq2jvOqUGzhU9W7334tP9OJuh/rMiG0Twr5X4L5yzr2pnO15wKUnWiZjTNXoenpj/H4fgeIASLTuzPiyjvKq42UCYCrwE+ACnJrGfGCCqh5NcNmMMbVY1qY8ikucfo6SkqpJchjeUW4Sx8sEwDeBHsBLOMvGdgdsISdjTIUsyWHd5WU4bldVPTfs9RwR+TpRBTLG1A3WdFR3eQkcX4pIpqpmAYjIAGBBYotljKnNwudTBOdvlDfHwuZe1D7lBg4RWYHTp5GMM2R2q/u6I6XThhhjTEi0+RRA1DkWNveidqqoxnFllZXCGFMnZOfk88Ls9VHnUwS3FYatBmirBNZOFQ3HzanKghhjarfwNCMK+MI6xT9b9R0BN39DQCGtYQpgcy9qK1sB0BgTF8Hag+IM1xx0VgseGHI2AJO+2Bw6ToD8w4WAdaDXVhY4jDFxEVl7eGDI2fTtmMbjH6+gOHA8XZw/Yha5zb2ofSxwGGNiUt4oqGi1h+ycfD5YenzlhGCmXAsUtZsFDmOMZ+WNgoo2/Bbc2eNubUOAG85vz6gBtsxBbWeBwxjjWXkZaMsbUhvZfHVdRrvqLL6JEwscxhjPoo2CqmhIrXV+100WOIwxnpUXCCoaUmud33WPBQ5jTEwiA4HXWkV2Tj5Tl+UiwLUZ7SyY1GIWOIwxJy0YTLJz8nl5zsao+ahumriQwhKno/zdJd/yy5E9raO8lrLAYYyJi8gRV2Ov7EH+4cJQP0hRyfG5HCVVtA65SQwLHMaYuAjvJC8sCjB22koCqqEgkuyXUI0Djq9Dbplyax8vCzkZY0ylwhdu8vmEgGpopFX+4UKm3D2Qy7q3xu8TfOJ0qId3pAdrLL+dtY6bJ2WRnZPv6X2DzWNejzcnz2ocxpiYRasZhHeSpzVMYfyMVRQWBRAR0hqm0LdjGq/e0q/cWsWJZMq1tOzVwwKHMcaz4MioD7NzKS4p+7COHHE1dtrKUH8GwKgBHcocE7zmngPHSPL7KCnxninX0rJXDwscxhhPItOmQ8UP6/zDhZQEFAWKy+kMjxxtleQXbuzfwfNwXUvLXj0scBhjPAlPmx4UbIaKJrNzc/w+CeWqitYZXma0VYnStlkDz7UGm5lePaxz3BjjSXjnd5LPyXQbUGX8jFVlOqaD/RijL0gnqZzO8OA1k/0Sen0itYa+HdO47+KzLGhUIatxGGMqFQwEwbkZ2/cdYcriraGhty/MXh9afyOyw3r8yJ6s3F6AAOu+O1CqdtC3YxpT7h5YakY5EHUSYaxltRpI4ljgMMZUKNrIJYCpy3IpLAoQABZs3MOSLXtDzUbhHdYrtxfw0bLcUkvKhneqh3eWn+woKRtlVTWsqcoYU6HyRi69PTqTQV1a4BNK7Qtv0kpO8iFQqm8kvJYS2cRVXtr2kymriT+rcRhjKlTeyKW+HdN4YMjZLNmyt9S+yA5rKF07ESAAzN+wh0Wb9zLlrrLrd4TP/4hHWU18WeAwxlSospFLF3Zpya79R7nh/A7lzucInxg4ecFmNu46CDg1kY+W5ZY6b+yVPULpSsbPWBVTPisbZVU1LHAYY8pV3pKwwX3hczBW7VjJqu0F9GjbNJTcMDKQZOfks3n3wVLXWbmtgOyc/NCx+YcLS6UriXVSn63/kXgWOIwxUVXW0Rw5B6O4RHl70dbQ6xS/MOXugWXOCYRPBAFWbCvg5klZoeuHNzf5fcL2fUdKBRZT/axz3BgTVWUdzZFzMCIVlihTl+WWe464X5HXDzY33dC/A4gwZfHWmJIemsRLaOAQkWEisk5ENorIo1H2i4i86O5fLiIZlZ0rIuNEZJuIfOV+jUjkPRhTX0WOjoq2JOyUuwcyakAHLnez3kaKGlZEEMDvF5LDrp/WMCWU5bZvxzTOaNaA4hIbIVUTJaypSkT8wMvAZUAusEREpqvq6rDDhgNd3K8BwCvAAA/n/l5Vn09U2Y0xZbPdhtcIwvs+fnVNLwDeWbSVJz5ZwV+SnuZC3yrnIl+7X64MYJ0f8DuvV6dm8EyrZ+jRpomTTTesWSyzc3OS/G6Tld9GSNUkiezj6A9sVNVNACLyLjASCA8cI4E3VVWBLBFpJiJtgE4ezjXGJFiwXyG8r+O2gZ2Y9MXm0CJNwb6JUbtf4MZTXkMUpJwWLAn9x9H96DLe3Ho563LaMbH4N2Xmg6Buh4hqtMtVymaRJ0Yim6rOAL4Ne53rbvNyTGXnjnGbtiaLiP01GJNAkSv7TZy/ieKAM+rpWFGAvIVvwbhmsPQ1fJQfNKIRcb66Si7fpIxifNLkULNY1qY8it3sukVR+ksqc6ILQ5nKJTJwRPvzifzYUN4xFZ37CnAm0AfYAfw26puL3C0iS0Vk6e7duz0V2BhzXHBlvbSGKREr+x0/5o3kp7ls7f9R9n/t2AQDyI+SZrPGP4q+BZ85TVVuv4kCH2bnxvTwt1nkiZPIpqpcoH3Y63bAdo/HpJR3rqruDG4UkVeBGdHeXFUnAhMB+vXrd3J/1cbUM5FDcYPJDdMapjB22kqKA8qnKQ/TTbZF7wA/Qc61AvDRXfTtdyfX97uddxZtRYGSktjmdNgs8sRJZOBYAnQRkXRgG3AjMCrimOk4zU7v4nSOF6jqDhHZXd65ItJGVXe4518DrEzgPRhTL0V+Ws8/XBiaALg17xAjs65zgkY8o0akpa/xv6ev5QP/GIpKFL9PYnr42yzyxElY4FDVYhEZA/wTZwzFZFVdJSL3uvsnADOBEcBG4DBwe0Xnupf+jYj0wam9bgHuSdQ9GFNflfdpPTsnn6uzvk9Xj0GjvKq+13jT5LsFTPfnMLzkN7F1nrhsFnliJHTmuKrOxAkO4dsmhH2vwH1ez3W3/yjOxTTGRIiWqPDlORu5bOk9dJFcz0GjMLU15x76Q+nZ53NvRTf/GyoYfRUkOB3nM1Me5sqi52xN8RrCZo4bY6IKrqwHznDcxp8/QpeDSyt92CuA+JBrX2XSgJllO6hvnc6z/bNYq2d4GmUrAt1kG39P/t9STVXBznsbLVX1LFeVMR7U5/kAWZvyeEwn8SP/bE9BQ1p0gzGLAMjMyY/a5LVqx34mFD7HVb4v+H3Sn/BJxbWP4JDdfW/2I/uWpQC2YFM1shqHMZWojfMBvH4a93LccJ3vLWgo7D99UChowPEmrwcv71rq4T68ZxsApgcu4MzCd9iuzSqtfQjQrHgP6a9156NluTbUthpZjcOYSpS3Al5N5XX51GhDbrftO0KT1ORSx3We/2ClvdkKrO9wA7M7/y+ZEZlso3VQjxrQAYAXP1/Pd/uPMajwT7yZ7KQqqazmkcZhHlp5NVOT/hiqyQTzXNXHGmF1sMBhTCVq23wAr4Euckb42GkrSWl/GBE5nsb8+W6ggQrfT3FqGiO3XEPhxnWem45GDehA19Mbc8OfF1IcUG4petwJHv5VFcYpEWhWsodlaU/wl/PeJa1hSpk8VxY8EsuaqoypRHnNLTVVZVltox3nzAhXVEEDbnqPPw5AD+6Iem6QKhxp0oW3ur54PAhV0HQU2TTWt2Ma40f2JJid/Zaix5lf0sNTs1XD/Ru4L/tK8g8XWrNVFbMahzEe1Kb5AF4nvkVmvx033ZlLq8CIL+9FfWsr/OSvwJGmXWj44FLSFm0NpSIJKFHXCi+vCW3UgA6s2l4QWgTqlqLH+UXSZG7x0q9ycAejF43gpaQ/nPA65SZ2VuMwpg4KH0pbUed38LhRAzpwfb/2CNBJvmOQrKw0aEiLbjR80BnhlH+4MHS8z30dqaLcUddmtCN8Tagni+/g/qKfeKp5pBzdycImj4dqTeNnrKoVAxhqMwscxtRCXkZDxToa7NqMdrTw7ed0ya/0k34ASo2eyuzcnFOSnWavlOTozWMVNaH17ZjGL6/uVWoxqOmBC/hZibfg0ezQN/wt6SFrrqoi1lRlTC3jddRUrKPB+nZM40y2Vfr+qpB17jMMiji3suaxyo4JdpaP/9sqvs4tAOBvJRdwQ6sdZO79uOIOc5xJgp+mPMw1+tsaP4ChtrPAYUwNU9lkw8oCQvD8YDp0z6PBnu8Gp1Z8iCr8tWQIB9IuKxU4vE6Q9NJXtGbH/tD3fr+PlJG/Z9d/GtFq3VuVjrbqJttYdPpLNO04vOIbMSfFAocxHlXF7HEvtYmKhgeXlw690jL/cQAc3AGntir3EFV4s2QITzOaKWHv+c6irYydtrLMioAnIrh4Ezi1iO/3bQfA91ZfyWP6XaUTEQVo+t0CmPEgXPm7EyqDqZwFDmM88No8dLK8NC9Fa/IJBrXt+46Umw69XG9cBXvWVniI4qwPvr7bOKZktAuV6Z1FW/m/T1aERlQVnuQEycigeF1Gu9DPZKzeAeBpFjtLX3P+teCREBY4jPHAywM9HjUSr5MNw5t8woNakk9I8vsoKfHYPPXGVbD535WWS05tQ4+H5vB02LbIoAHgk9jWzIhUXj9I8GcyPnAn4C146NLX2J+7lqb3lkmybU6SBQ5jPKjsgR6vGkl5D87woASU2h8e1EoCyg3923NGswbemqcqqWkAcEpTeKj0cdk5+W7z1PFtPoHxI3uedE0sGBSDI8cyOzdn7JU9+HTlDpo3SmH88jtRqHSehwBNdixg+4tDafvTf55UmUxpFjiM8aCyEUGxjGCqrGYS2YEcDErHigL43FneJQEtlV8qvJZxXVhTUvj7pTVMOd7f8cFAp0+jEgHA9/OtZbZnbcqjJCxq+ASeurpXKAfVycrOyeemV7MoKg7g9ws+EYqKA6GFoZ4M3EG67PCU26pNXhY7p9xH65tejkvZjAUOYzyraESQ1yamE6mZZG3K41iR89AsUSgpcR6fwfxSAVWSfMKN/TtwbZSgEQw6ivOAn598H+rLr3QVPlV4WMcwKiJpYfB+T0n2cawogAjcfWHnuAUNIJT9FqC4RIm2luAtRY87655T8WqEItBq3Vsw4xTr84gTmwBoTBwEayQ39O/AdRntyj2uVGJBjxPVDhwpiroEa3CmdEChqERRtxzR3k+Bq3xfsD55FG3FW9CYH+jBJ0X/FbWMfTumMfbKHqEJe68v3BLX2dq7Dxwr9Tp8YmC44YXPeVoQSnD6PFj+fpxKWL9Z4DAmjj5alsuUxVtLzdQOn+Wd1jCl0pxO4bJz8pn0xeYy20Vg9AXpJLkPVAU+zM4t8/BOa5iCAL9Imswfkv9Eks/b0t1r9QxuKXocKsj9lH+48HjgisNs7eDP6Z1FW5m7bldoe5JfuOuC9FIpSQDOanUqv7qmF4fvXMB3p6SXu755kACBj+46qTIahzVVGRMn5eViCm+aui6jnfPpF+dBtnJ7QYV9HpF9CUECNG6QzPX92vPOoq1OM1ZJgKnLckt1oo+fsYoZyQ/TTSpuzglXQCOGFzrjp0oCTu6nrqc3jmk+SazCm/B84tSkgvf5g37tadwguUxgyM0/zNa8Q3y6cgfDL53OqCXXo3sqTswoAL/uAFH6bYx3FjiM8aiikU1w/EEanqX1o2W5HC1y2uqDTUbJfqHQbVp6f+m3fJidS3GJM5T2+n7tQ/0U2Tn5bse3UFSiCMdrCynug3rddwfwu01Wfp+ErpWS5GN6k+dZ43OSEHoJGqqwU9PYrKeX2l5Y5H0+yYn+PMODrqoi4vTHBINt8J6DfTUAx4oCTJi3CYD5G/Ywt/vLPJF/B+2Kc8q9XwE4VgDjmsG1E6H3D2Iqs3GIelktvpbr16+fLl26tLqLYWqx8NnRSe4C2cEHdPjs7HXfHSh1XIkqJWHrIN17UWcOHCsO1RKCz7fw/wuTfJDRIY3srfsIBDR0XLJfGHdVT1ZuL0CAHm2bMn7GKo4VBfD7hEu6taLhuqn82v9nUqUEpNKF+0JUnRX8bvXnsf9wEYe33lNqf/9OaTwy/Jy4TXqMNsN9/IxVFBYFCLj36/cJ40f2DHW6Z+fkM3VZLu8v/TY0QCDy6eUXYqthXfuqBY8KiEi2qvaL3G41DmMiRDYdBecsBFNhOB3RzvfHwkY2BT8dB9v9C0vKfiib9MVmxo/sySnJThOP3ycECI4cchQHYPGW0n0VitNstHJ7QWjEUbBJZ1zSZGdC3CYgyVvtIvLa05KGMzP5bpqn/o4DR4tDzWlBi7c4w2On3BWfGfORzXr5hwt5e3QmL8xez4KNe0I1j2B69uDvpGfbprzPt6WCbriAOh3mC1J+Qlv2Vf6z+OguCxwnwAKHqVcqm0MRbbhsef0McPyBrhCaZ5Dkk6hBA/fYT1fu4LaBnVi1Yz892jRh8n+2EG24aSS/34cAj+kkfpgyu9SDM/SAjCFoqEIA4WdFP2Z64AJYvZNGHQ9xetNUtogz9DdcPNdbj9Y/0rdjGg8MOZslW/aW2h7+OxGOlyu8eD63diXizHEZVPgnlqXcSZocqfxHYs1WMbPAYeqNaEEByp+FHXxQHjhSVOo64n4cD37q9fsEVQ1NvhMINUVFUpz2+Pkb9uATWPhNXqgjOOjN5Ke50Leq7MkCfA34Y69VlCmHwnZtxqDCP5XaHlBl76HCqGWP53rr5fWPRNv+8pyNod9JebctwI39O9CjbVPGTV9JYYmSUfgay1JGk+Y7XEnwUKfmsTXL5nl4ZIHD1BuRQWHqstxQs0/4LGy/T9ASxe/3ceBIUagDNkRLB4vIDLTBDuvigHKV7wueTTre5+ChYgFUEhhOImgEY9SbJUN4sviOqMccLSoJPaST/cLgrq1o2fiUMpMLT1Z5Eyojt4fXTvw+cWp3EdWh4BTB/MOFDO7ais9W70SB84smsaLBPTQMHKi8QEtfg7yNcOv0k7qv+sACh6kXsnPy+erbfYgIPpwHvsDxyXhuX0VJWGc0qiyMMjchJdkJFiu3F/Dfa37E6Z+68yz+7Ty8blK4Kfn48aWCwEnWFE6GqjM/Y3jhc1H3N0rxE4jYdsegdB4dcU7iC1eByFoIEKoJTvpic2ggQvjotOQkJwWL3yc83etTxq0aQXLx/kreCSfh4/PdyuTmMqXZqCpT52Xn5HPTxIWhfge/T/jlyJ50Pb0xN09y8iFB2TZ9v8Al57SmwdqppWsNQe7xJ9tslEjq/ucYyTxafBfTAxcQ2V3jF0CgJAANOvwZgCPuqKokn/DePQMTtv7IyQpPJz9l8VYC6vR33NjfGYkVHkyWpdxFIz3oMXaL9Xtgo6pMPZa1Ka9U00Yg4IzWCX6Snbosl/eWbAWldNMSwCYgpZyKQg0NGOGfBbOkF6OO/RzFCRCXntOazbsPsvdwEfluX4Yzgin6tUoCGrcO8UQINmu9s2hrqRn5Pds2Jf9wYWjAQmGJ0uvIRLI99XlAqN/jkx/D1a/U+wASyQKHqfMyOzcPTbqDsE7e5e/T95P7yAgU8nR5TUu1gIaNTS1WP/9TdI8zSipMsBP/3+t3R23OKQ5omZoIODWWytKi1AT5hwtDXUg+93Vaw5RSXUox93kABIqdADLvtzBmUdzLXVtZ4DB1Xt+OaUy5eyBt376Y0wvd/oi/HB8VJVArag8hYbMGFXirZAhr+47j272H+WLDnqj9762bnELrJqms2FYQdd2Odd8d4IlpK92fhRx/CAuhuRTVLTgBUKBMR30wW2/4MN6sTXn4hFIB0e/3sebWFZ7TyofsWQvjmjrf97uz3o++ssBRgapYY9rE2fL34ZP7IHD8YadAhvt9dceHUO3AY9fi/EAPJ9lghMgHopQz/Dco71AhO/cfCx2jCnsOHAut3dG3YxpdT2/Mzxe+SbLPxzfb45ODKl4i+6k+yM4tNRmxopUDgylfgONDnx9a63n1wzKWvnZ8adr079XLUVgJDRwiMgz4A+AHJqnqMxH7xd0/AjgM3Kaqyyo6V0ROA94DOgFbgB+oavzyObs2/esvnP7vp/gxe9gxtwWbvvd/dL7k9ni/jfEqSkDwqqqChZdxJm+WDGF2+v/ywJCzQ5+eDx0r5pOvtld4nkjp60c2K0W+tQBntmxEo1OSaN0kldlrdpY6JgDMWr2Tuet3hx7AfTumccaaBgCMP4kcVIkQ2U8VbTJi5DDevh3TuG1gp1LDqYtLlI+W5TrH3Trd+bs6mYy5m/99vCYSzn8KjPxjne0bSVjgEBE/8DJwGZALLBGR6aq6Ouyw4UAX92sA8AowoJJzHwU+V9VnRORR9/UjcS388vfpMO8hkqQYgDPYg857AOY9ENe3MbVT6AEeMcJqfqAHd5Q8zg3nd6DxKUlM+mIzJQEnOWH4MNdf9WxT6iH38pyNpWoQZ7VsxDd7DpUKFGe2aMTmPYfKjPyKJpgc8NnvnxtKmTJvw+5SCQKDypsNXtGiVdWh3H6qSqzaUXYIbqmfQe8fOF+/7uAkP4yXkmNOQKopadzjHMgSWePoD2xU1U0AIvIuMBIIDxwjgTfVGROcJSLNRKQNTm2ivHNHAoPd898A5hLvwPHpIyRRXGpTdTdxmOpRphYhsDo1g8ca/5KBnZuz/1gxe9xFh1o2PoXxbZsyZ90uVm4r4Mrebcg7VEiPNk34Zs8hVm8voEFKElvzDvHynI2kNUxh5fYC9hw4RpLfR3FxAJ9PGHJOa5LX72bNd8c7cJP9Pi49pzUA+w4XhhIgQukHYWqSj9ZNUkn2C3/+9zcM7tqKldsLuKhLS/YdLmRpTn7p2orAhp0HeOzjFQhwQIppnFrzWrCD/VTl9XFEk52Tz9GikjLbpyzayvtLnKY9dYfvikzgCd9r/NA3u1QW4jqj5Bh87CaujEPwSNg8DhH5PjBMVUe7r38EDFDVMWHHzACeUdUv3Nef4wSBTuWdKyL7VLVZ2DXyVbXCv6CY53FEq3qaOi/yfwUF/lrBDOtISX5nNnlJ5Cw6D/w+AAllw63oPYLrb/vC5l94JTgPynZpDcnZe7jM/kYdJ3JOmyZ8ePVfY7uBGiayTyQW4Slf6lwAadoefrbS8+HVMY8j2o88WlNstGO8nFvxm4vcDdwN0KFD/NZCNnVDtM9L5XVEe1V8Ag+poJIACBUHjeB7BI+raP5FeUKtbOU8EIuPtiElUHOaqE5UZJ9ILIJ/A3UygBTkxuUyiQwcuUD7sNftgMgewPKOSang3J0i0kZVd7jNWruIQlUnAhPBqXHEVPIGp8GRvTGdYmqm8h6sJxskojmZGkewJlFcHCiT9iPaccH5F5Ep2SvjE6d/YFiP08vm4AI0byT/c21m7DdQw0T2iUSOQvMi+Pfxi2Daend7rQ4iTdvF5TKJDBxLgC4ikg5sA24ERkUcMx0Y4/ZhDAAK3ICwu4JzpwO3As+4/06Le8mHP1tzOrVMVF4/aX+hPbmj5HF8IjRI9tGicSptm6ayclsBp4rzkPbhdF43SvGT3qIRXVo3pkfbpqzcXsDGnQc4VhwgvUUj8g4VMrxnGwA+XbmDHm2ahPo4gkkAASb8+xt27T/KwM7NadwgmbSGKeQfLuTAkaJQKvXg9uCiTMFzszblldoeLEfke4TnbZq6LJc9B47x7d7D5B8u5OzWjSk4UkTrJqmhPo7gtcKTMXZo3qjUfXjtO6gNovWJfLbqO95cuIWjbjNf6T4OZ22T4OvwfU/pnYwvugNVGOn/gl/7/8wplO07gRoeVMQHl46Nz6USmatKREYAL+AMqZ2sqk+LyL0AqjrBHY77R2AYznDc21V1aXnnutubA+8DHYCtwPWqWmH14IRyVZ3E8E9TBWwSlqlpTnReSFU4wVFV5fVxWJJDY4wxUZUXOHzVURhjjDG1lwUOY4wxMbHAYYwxJiYWOIwxxsTEAocxxpiY1ItRVe68kJwTPL0FsCeOxakN7J7rB7vn+uFk7rmjqraM3FgvAsfJEJGl0Yaj1WV2z/WD3XP9kIh7tqYqY4wxMbHAYYwxJiYWOCo3sboLUA3snusHu+f6Ie73bH0cxhhjYmI1DmOMMTGxwOESkWEisk5ENrprmUfuFxF50d2/XEQyqqOc8eThnm9273W5iPxHRM6tjnLGU2X3HHbc+SJS4q5kWat5uWcRGSwiX4nIKhGpoSlevfPwt91URP4mIl+793x7dZQzXkRksojsEpGoy/vF/fmlqvX+Cyd1+zdAZ5xFpL4GukccMwL4FGd1wkxgUXWXuwru+b+ANPf74fXhnsOO+xcwE/h+dZe7Cn7PzYDVQAf3davqLncV3PNjwLPu9y2BvUBKdZf9JO75IiADWFnO/rg+v6zG4egPbFTVTapaCLwLjIw4ZiTwpjqygGbuCoS1VaX3rKr/UdV892UWzkqMtZmX3zPAfwNTKWd1yVrGyz2PAj5S1a0Aqlrb79vLPSvQ2F0T6FScwFFctcWMH1Wdh3MP5Ynr88sCh+MM4Nuw17nutliPqU1ivZ87cT6x1GaV3rOInAFcA0yownIlkpff89lAmojMFZFsEbmlykqXGF7u+Y/AOThLUq8A7lfVE1j0t9aI6/MrkUvH1ibRFnyMHG7m5ZjaxPP9iMjFOIHjgoSWKPG83PMLwCOqWiI1eh1Qz7zccxLQF7gUaAAsFJEsVV2f6MIliJd7Hgp8BVwCnAl8JiLzVXV/gstWXeL6/LLA4cgF2oe9bofzSSTWY2oTT/cjIr2BScBwVc2rorIlipd77ge86waNFsAIESlW1U+qpITx5/Vve4+qHgIOicg84FygtgYOL/d8O/CMOh0AG0VkM9ANWFw1RaxycX1+WVOVYwnQRUTSRSQFuBGYHnHMdOAWd3RCJlCgqjuquqBxVOk9i0gH4CPgR7X402e4Su9ZVdNVtZOqdgI+BH5Si4MGePvbngZcKCJJItIQGACsqeJyxpOXe96KU8NCRFoDXYFNVVrKqhXX55fVOABVLRaRMcA/cUZkTFbVVSJyr7t/As4ImxHARuAwzieWWsvjPY8FmgN/cj+BF2stThDn8Z7rFC/3rKprROQfwHIgAExS1ajDOmsDj7/nXwKvi8gKnGacR1S11mbNFZEpwGCghYjkAk8CyZCY55fNHDfGGBMTa6oyxhgTEwscxhhjYmKBwxhjTEwscBhjjImJBQ5jjDExscBh6h0RGSciD7nfjxeRIRUce7WIdK9g/70VpegQkU4iMurkShy61mAR+a8K9g8TkcUistbNdPueOxcnmB31/0Rkg4isF5E5ItIj7NwtIrLCzRY7S0ROj0eZTd1kgcPUa6o6VlVnV3DI1UDUwCEiSe48iDcrOL8TThLBeBiMk7E4Wll6Ai8Bt6pqN1XtA7ztvj/Afe6556rq2cCvgekikhp2mYtV9VxgKU72WGOissBh6gURedxdn2E2zizh4PbXxV1zQ0SeEZHV7noFz7uf7q8CnnM/wZ/pJgL8lThrVtwfUXs5S0Rmu5/al4nImcAzOLOyvxKRn0WUabCIzBORj933nSAiPnffMPcaX4vI5yLSCbgX+Jl7rQsjbvER4FeqGprxrarT3aypwf3/raqH3X2zgP8AN0f5cc0DzjqBH7OpJ2zmuKnzRKQvTtqJ83D+5pcB2RHHnIaTFbebqqqINFPVfSIyHZihqh+6xwE0U9Xvua/HhV3mbZz8Rx+7n+R9wKPAQ6p6ZTnF649To8kB/gFc6walV4GLVHWziJymqntFZAJwUFWfj3KdHkC07YhIE6CRqn4TsWupe16kK3EyxhoTldU4TH1wIfCxqh52s59G5i0C2A8cBSaJyLU4aRnK817kBhFpDJyhqh8DqOrR4Kf7Six2140oAabgZCDOBOap6mb3WhWts1CGiDR3ayXrg7Wh8g6ldIbUOSLyFdAEpynLmKgscJj6osLcOqpajPPpfypOv8Y/Kjj8UJRtJ5qDPbJcStkHuhercFaAQ1Xz3D6OicCpbrA8JCKdI87JwFn5L+hiVe2jqreo6r4Y39/UIxY4TH0wD7hGRBq4NYP/F3mAiJwKNFXVmcADQB931wGgcWVv4D6cc0Xkavd6p7iZZis7v7+bxdUH3AB8ASwEvici6e61TvNQlt8Aj4vIOWHbGoZ9/xzwoog0cK85BKd2805l92ZMJAscps5T1WU4zUtf4dQo5kc5rDEwQ0SWA/8Ggh3Z7wIPi8iXbmd3RX4E/NS9xn+A03Eyzha7ndw/i3LOQpwO9JXAZpwmtd3A3cBHIvI1x5vG/oYTAMt0jqvqCuB+4E13OO4CnBXugoHhJZx04ytEZB3wBDBSVY9Uck/GlGHZcY2pJiIymIo7zo2pkazGYYwxJiZW4zDGGBMTq3EYY4yJiQUOY4wxMbHAYYwxJiYWOIwxxsTEAocxxpiYWOAwxhgTk/8P3iHu6cibVX4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 0.979 0.966\n",
      "fractional expected GOP seats =  7.278  out of  8 seats for TN-noMemphis\n",
      "simpler expected GOP seats =  7.2919  out of  8 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOTE - I DID NOT PULL MEMPHIS BACK INTO THIS ANALYSIS. (can be pieced together if needed)\n",
    "#I manually corrected responsiveness and stateGOP2 for the added Dem district"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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